diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..eceaa070d8965d25067e862c10dae7c7b50d5ccf 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,60 +1,584 @@ *.7z filter=lfs diff=lfs merge=lfs -text *.arrow filter=lfs diff=lfs merge=lfs -text -*.avro filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text *.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text *.ftz filter=lfs diff=lfs merge=lfs -text *.gz filter=lfs diff=lfs merge=lfs -text *.h5 filter=lfs diff=lfs merge=lfs -text *.joblib filter=lfs diff=lfs merge=lfs -text *.lfs.* filter=lfs diff=lfs merge=lfs -text -*.lz4 filter=lfs diff=lfs merge=lfs -text -*.mds filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text *.model filter=lfs diff=lfs merge=lfs -text *.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text *.onnx filter=lfs diff=lfs merge=lfs -text *.ot filter=lfs diff=lfs merge=lfs -text *.parquet filter=lfs diff=lfs merge=lfs -text *.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text + *.pth filter=lfs diff=lfs merge=lfs -text *.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text *.tar filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.mat filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.hdf5 filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text *.tflite filter=lfs diff=lfs merge=lfs -text *.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text *.xz filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text -# Audio files - uncompressed -*.pcm filter=lfs diff=lfs merge=lfs -text -*.sam filter=lfs diff=lfs merge=lfs -text -*.raw filter=lfs diff=lfs merge=lfs -text -# Audio files - compressed -*.aac filter=lfs diff=lfs merge=lfs -text -*.flac filter=lfs diff=lfs merge=lfs -text -*.mp3 filter=lfs diff=lfs merge=lfs -text -*.ogg filter=lfs diff=lfs merge=lfs -text -*.wav filter=lfs diff=lfs merge=lfs -text -# Image files - uncompressed -*.bmp filter=lfs diff=lfs merge=lfs -text -*.gif filter=lfs diff=lfs merge=lfs -text -*.png filter=lfs diff=lfs merge=lfs -text -*.tiff filter=lfs diff=lfs merge=lfs -text -# Image files - compressed +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text *.jpg filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text *.jpeg filter=lfs diff=lfs merge=lfs -text +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text *.webp filter=lfs diff=lfs merge=lfs -text -# Video files - compressed +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +*.wma filter=lfs diff=lfs merge=lfs -text +*.aac filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.m4a filter=lfs diff=lfs merge=lfs -text +*.m3u8 filter=lfs diff=lfs merge=lfs -text +*.amr filter=lfs diff=lfs merge=lfs -text +*.audio filter=lfs diff=lfs merge=lfs -text +*.avi filter=lfs diff=lfs merge=lfs -text +*.flv filter=lfs diff=lfs merge=lfs -text *.mp4 filter=lfs diff=lfs merge=lfs -text -*.webm filter=lfs diff=lfs merge=lfs -text +*.mpg filter=lfs diff=lfs merge=lfs -text +*.asf filter=lfs diff=lfs merge=lfs -text +*.mov filter=lfs diff=lfs merge=lfs -text +*.mpeg filter=lfs diff=lfs merge=lfs -text +*.3gp filter=lfs diff=lfs merge=lfs -text +*.wmv filter=lfs diff=lfs merge=lfs -text +*.rmvb filter=lfs diff=lfs merge=lfs -text +*.rm filter=lfs diff=lfs merge=lfs -text +*.ts filter=lfs diff=lfs merge=lfs -text +*.mkv filter=lfs diff=lfs merge=lfs -text +*.flash filter=lfs diff=lfs merge=lfs -text +*.vob filter=lfs diff=lfs merge=lfs -text +*.pdf filter=lfs diff=lfs merge=lfs -text +*.ost filter=lfs diff=lfs merge=lfs -text +*.pst filter=lfs diff=lfs merge=lfs -text +*.doc filter=lfs diff=lfs merge=lfs -text +*.docx filter=lfs diff=lfs merge=lfs -text +*.txt filter=lfs diff=lfs merge=lfs -text +*.ppt filter=lfs diff=lfs merge=lfs -text +*.pptx filter=lfs diff=lfs merge=lfs -text +*.xls filter=lfs diff=lfs merge=lfs -text +*.xlsx filter=lfs diff=lfs merge=lfs -text +*.vsd filter=lfs diff=lfs merge=lfs -text +*.vsdx filter=lfs diff=lfs merge=lfs -text +*.jsonl filter=lfs diff=lfs merge=lfs -text +*.json filter=lfs diff=lfs merge=lfs -text +dataset_infos.json ignore +*.csv filter=lfs diff=lfs merge=lfs -text +*.tsv filter=lfs diff=lfs merge=lfs -text + +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst filter=lfs diff=lfs merge=lfs -text +lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c69d0b2a93dac6d254eb984955169caa229f2cef --- /dev/null +++ b/README.md @@ -0,0 +1,9 @@ +--- +license: mit +tags: + - large-model-feature-coding +models: + - facebook/dinov3-vit7b16-pretrain-lvd1689m +language: + - en +--- diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..d30cbc570cbfd296e601b29a1f5a964618e240ec --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9262b9904e1076e1dae093875719c1019f4fac62fa9aa72966f1ea28f283b92e +size 85916017 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..a67df54707921b41988ca9e62ccef53c731518d3 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e38fbffc2797b50a21bfc7c41bdb335f12a8e8e4e375d1e43282805f3ceef296 +size 82242914 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..b73939b504c665a8fecd4784a370bf8773976e34 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36ebbbd0338625da79536161aedc8f91e2312106a4d370e878c43b1d61e88bbb +size 84576057 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..b953df19bd5b84b8844a61f7ee46cace0f5f4678 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:83648daafaea72c39b9548f111073e56d26d19177f06976684b9adc0d2f4a798 +size 83770515 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..be42677dc80b713d84db2da74f21dbc8411a148a --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de701c648a0009a0875bf7cde8a1b7e3dcc1ba9ad4aab51dca7f47e1e7ac4905 +size 85910050 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..ee501c35271836ab306e48e38906e38cf912f8e0 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:30aad94da89b07713887b419578bb4697c26b3b715cd4f0ad501bc51d3506af9 +size 85320065 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..9835303bbbac6c7c739b2a698ee1873c0b1cd491 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9e327209a4b8d0df5a7cbdf74c0052201bd6c789bf84d49ef0b0e97e081ffa8 +size 83578542 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..e8b85e41900637b2fb0daee7c7b84ef4a7f3485b --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc4208b278918428c936a9157519a7d79fa6eb8aee0e5fb0a80818f705929924 +size 83417537 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..b9ee68b2103c8e6c54e1047f2f570d8852895da4 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0c8c8e07b8eadba86026163f596514927de2f25d9ac041496621bb46938385d +size 82106447 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..6de90985169cbac7254cb946895452937444b5f9 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5323b072e770216d701a3f08dcec3d0178f95a847acb7ff0e4af110791dada08 +size 85513248 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..6b84378659069b3f0877b0e483beadd3c4b2d859 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34ade9006e1d9740f0f7120b8cdf382f9f80977e2366dcc8d532d733d682d138 +size 85541164 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..6f11f17e8313291522e16f3769aafb96d1d519b0 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f970f7b95198ff0023b4629d1f1ade8233d56808be49fb3b1da46de3cf675d2b +size 83836107 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..1c7fba7b39c631dfa47e60911c43d7768b73336c --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c351866fca600cbd9e648d140e76ff36e41e500892c6243d3a827776ac70cb19 +size 86123779 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..0e3607ef1e55c5d67a5b5b9945345e3c9d48671b --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47c2f5cbcbacd1ac2e570bacdf85a3fb080a2dba77014642ee7d1c9ca8a6ff9e +size 85319707 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..c711c9d52f0b631b78cc7a7563d64fd957ecf427 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8518a6617bffdd250a2faa5b3a3849dcb329e30e122776cb34a02c339c2470e8 +size 85380571 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..d5d192c0beb3242efa9fe665aaafe2c14083e588 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68e8a106160b3cbc18c9af9cf7489aea17d8b2f55796f5ada84855d6111a9d1c +size 84151772 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..4b718c9368d9491537c89c8cc708d7e707d90b96 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99d8a073efcf8a9088a6212dd26e0c08d429de00f9a1d3e6b10fe265d7e3711b +size 84116453 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..1906c93e89a56056b57f8856acff6a6db78fd099 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34c09f36f529aaf41debd2de952dc62ca2ce43c09eea53171fc77dd39ee7ab45 +size 82700528 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..634a965843590a6a15e385ec598c535482e9c137 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0159b87956297ab07dd12a6a04e0b5fd6c0dc00761a92a31526040e1110860f0 +size 85080989 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..c4403d522db1644cfd9e65c16c52f586459c9605 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ebe73f79d4c3c8cdcb3b367a30500e8d238d7f3c947a507e9b4a1b84faaaa4c +size 84638307 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..9751fbe50b02436c05a4f120456fec31581c6145 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0cdad0490cf9b7a8e039a9a53fc06e98a3ae43e5e2b7421d201fd94ff51d35d +size 84864617 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..3cbd02ce7d3bb8c5ce1c270ab9538958b407335f --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88bb77fea89414ef3c5efdc503e36910e76b67c6744a50e4e9a20118711352d1 +size 85604862 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..350991d06c05a560e659bbfa5cce52e5bdc3fd91 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85026358a869b3af16cb17b3c90c77ab5e238d355cf30e4ff47e42d7a3d74066 +size 88282773 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..4bb322e5c21ebcbf972ad0a2dcc80fa544ad3547 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1db93a625953494c1aa49020dbcd635b1efefd81a493f1ac40524ee0aa8ca5e3 +size 81283190 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..caba175a6bb309fc3ef8a72307f20368106d7268 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa0d5490b270b88db9b519e690a12bd8af5dcc33a9b62e985450d1cc243bd75a +size 82170389 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..ca23b4e088abe540be6dba2de3255044608bfd0c --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:77d0a4483b1394d9bf67a4acd16478cc1c407efdb933f7b9898693b98556dd2f +size 82940156 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..035f2f9e5b03ea4b8536f8dc1e87fae3d0e5957b --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3376e6b4468ac3c1638927988d56d91988e6b64de8011c00453ee11d3990fae4 +size 81648272 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..66f2f4aebadbb76d75f5b4f3cf891ca023b7ded5 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c13da0cf5dbb8ebaa23867a22a90964c6eb2133c5a275f9859871d6c1f4615b6 +size 84266637 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..6cadb53885cb84dc78aa33e114c2107460f953f3 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05a5da1bb4eb16897beccf4e76b51d07264ae26a717e117ae5f911da769c55c7 +size 83935994 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..647bd99fb9d6601db377c90bdb1e2a66d613994c --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39ab3c28fe3eea0316dc3aef317807bfaf53965b5a645cd04f0e065d6ccbd4d5 +size 81774046 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..4a81a115e00840933a72576024a72539ff36f06f --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ecb1eb635619c7eaaaa619fd6d2be7de97fc7b019be0392e63e8c17c929a869 +size 81264481 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..186bf5e958389dc14849b4ad95434c71d4618d9d --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:237f083bebb6bc9bfa26facfd2a8d1a0a658c134eff0645f9505a9d45fce9f9b +size 82157262 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..2a5b1db56f7ca784bb689d9599f307a976f02014 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51cf604bf82966b66e28220feda179f3fa9638505b0e8a9ea3c769f63b382ab2 +size 83250392 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..0f68d4d4ef5a032d96586867b75d7765a065b592 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c993ba96a825b8688c9db0274949701d8b366a87bc757c985ed355fe69660764 +size 84787652 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..02cffb7b89c7b3a713083f29a1c2056609d24a5a --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7469e4f7585a3fc30a4d12c1d8036a7229506588c88ed6a82fdd78962409452 +size 84269114 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..c0070052db4bf2d7cbd025549a09092f5a9203ac --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:61e40f994cc00ace9164b4bafd549267fe64677e9b19943d8571a29f161480e8 +size 85472525 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..85d2969aeef680f97fab261794fcacccd841d66f --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb3ebb7877283cf2db7dc7e9c70e46582909f5f8c873517ffda03e0690e9c28d +size 84369584 diff --git a/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..b5aa0a9d4d2f554a7c2d1b3af6ce2d2dafa0d419 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 286 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.185s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,024B, BPFP=0.0254 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,016B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,952B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,564B, BPFP=0.0384 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 114,612B, BPFP=0.0727 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,644B, BPFP=0.0721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 132,688B, BPFP=0.0842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 141,888B, BPFP=0.0901 +⌛️ [2/4] FRONTEND: Frontend time: 33.747s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.687s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561056 5.37760374 + layer.9.1 0.14522085 5.33187828 + layer.19.0 3.25142184 22.84642357 + layer.19.1 3.25206135 22.90990362 + layer.29.0 4.23946030 198.64646978 + layer.29.1 4.24539299 196.44934189 + layer.39.0 32.17105490 14931.91160221 + layer.39.1 19.15684032 16105.22716932 + ------------------------------------------------------------------------------------- + TOTAL 8.32588289 3936.08754905 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 704388 +BPFP 0.0559 bits/point +EBPFP 0.0559 equivalent bits/point +MSE 3936.087549 +---------------------- ---------------------------------------------------------- +Time: 66.619s Load: 1.185s, Pack+Encode: 33.747s, Decode+Unpack: 31.687s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3936.0875 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.098s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,708B, BPFP=0.0239 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,252B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 55,512B, BPFP=0.0352 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 55,552B, BPFP=0.0353 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,224B, BPFP=0.0643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,152B, BPFP=0.0642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,276B, BPFP=0.0827 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 128,816B, BPFP=0.0818 +⌛️ [2/4] FRONTEND: Frontend time: 32.863s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.195s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03307773 5.26137662 + layer.9.1 0.03291117 5.40027002 + layer.19.0 0.04156009 23.67319782 + layer.19.1 0.03760627 23.89269682 + layer.29.0 4.28582750 200.49687195 + layer.29.1 4.28551552 198.15888040 + layer.39.0 9.83402183 15711.76860578 + layer.39.1 9.85397836 15504.29249269 + ------------------------------------------------------------------------------------- + TOTAL 3.55056231 3959.11804901 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 648492 +BPFP 0.0515 bits/point +EBPFP 0.0515 equivalent bits/point +MSE 3959.118049 +---------------------- ---------------------------------------------------------- +Time: 65.156s Load: 1.098s, Pack+Encode: 32.863s, Decode+Unpack: 31.195s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3959.1180 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.015s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,776B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,572B, BPFP=0.0245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,396B, BPFP=0.0371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,740B, BPFP=0.0379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,860B, BPFP=0.0786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,076B, BPFP=0.0794 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,592B, BPFP=0.1178 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 186,120B, BPFP=0.1181 +⌛️ [2/4] FRONTEND: Frontend time: 32.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083900 5.21490056 + layer.9.1 0.00259629 5.19624304 + layer.19.0 0.00955961 24.51630291 + layer.19.1 0.08538111 24.71990017 + layer.29.0 0.11631418 241.63743094 + layer.29.1 0.11200302 235.04367078 + layer.39.0 14.47657393 16131.58791030 + layer.39.1 13.08093694 16463.20831979 + ------------------------------------------------------------------------------------- + TOTAL 3.48552551 4141.39058481 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 816132 +BPFP 0.0648 bits/point +EBPFP 0.0648 equivalent bits/point +MSE 4141.390585 +---------------------- ---------------------------------------------------------- +Time: 65.031s Load: 1.015s, Pack+Encode: 32.509s, Decode+Unpack: 31.507s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4141.3906 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.205s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,652B, BPFP=0.0239 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,632B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,540B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,848B, BPFP=0.0374 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 119,592B, BPFP=0.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 119,340B, BPFP=0.0758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 116,876B, BPFP=0.0742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 121,216B, BPFP=0.0769 +⌛️ [2/4] FRONTEND: Frontend time: 32.714s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.857s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.70169554 5.26991305 + layer.9.1 0.03294074 5.29589844 + layer.19.0 3.25671692 23.93862224 + layer.19.1 3.25834093 23.74602647 + layer.29.0 0.10810242 220.72842866 + layer.29.1 0.10661203 219.37810773 + layer.39.0 8.95005916 15119.43581410 + layer.39.1 8.98756017 14865.85115372 + ------------------------------------------------------------------------------------- + TOTAL 3.42525349 3810.45549555 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 670696 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 3810.455496 +---------------------- ---------------------------------------------------------- +Time: 64.777s Load: 1.205s, Pack+Encode: 32.714s, Decode+Unpack: 30.857s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3810.4555 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.130s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,336B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,016B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,460B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,768B, BPFP=0.0392 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 138,484B, BPFP=0.0879 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 141,308B, BPFP=0.0897 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,760B, BPFP=0.1090 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 172,164B, BPFP=0.1093 +⌛️ [2/4] FRONTEND: Frontend time: 31.862s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.239s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527870 5.24332815 + layer.9.1 0.14521496 5.37164471 + layer.19.0 0.03964342 24.73214576 + layer.19.1 0.03956446 24.94553593 + layer.29.0 0.12258449 245.46228063 + layer.29.1 0.12735008 243.55447676 + layer.39.0 32.94776263 16775.35391615 + layer.39.1 29.25669534 16671.52811180 + ------------------------------------------------------------------------------------- + TOTAL 7.85301176 4249.52392999 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 825296 +BPFP 0.0655 bits/point +EBPFP 0.0655 equivalent bits/point +MSE 4249.523930 +---------------------- ---------------------------------------------------------- +Time: 64.231s Load: 1.130s, Pack+Encode: 31.862s, Decode+Unpack: 31.239s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4249.5239 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,032B, BPFP=0.0254 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,996B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,400B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,044B, BPFP=0.0387 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,484B, BPFP=0.0784 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,332B, BPFP=0.0777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 137,820B, BPFP=0.0875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 141,316B, BPFP=0.0897 +⌛️ [2/4] FRONTEND: Frontend time: 31.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.926s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66816044 5.33009272 + layer.9.1 2.66817504 5.34871501 + layer.19.0 3.22262959 24.03184666 + layer.19.1 3.22037432 23.91506287 + layer.29.0 4.30448692 220.62999675 + layer.29.1 4.31085282 216.63810123 + layer.39.0 38.33931691 15985.82255444 + layer.39.1 57.25219370 16672.38479038 + ------------------------------------------------------------------------------------- + TOTAL 14.49827372 4144.26264501 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 727424 +BPFP 0.0577 bits/point +EBPFP 0.0577 equivalent bits/point +MSE 4144.262645 +---------------------- ---------------------------------------------------------- +Time: 63.507s Load: 1.078s, Pack+Encode: 31.503s, Decode+Unpack: 30.926s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4144.2626 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,916B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,956B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,400B, BPFP=0.0364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,816B, BPFP=0.0367 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 109,540B, BPFP=0.0695 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 109,124B, BPFP=0.0693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,528B, BPFP=0.0771 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 123,660B, BPFP=0.0785 +⌛️ [2/4] FRONTEND: Frontend time: 32.755s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.900s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332920 5.51577924 + layer.9.1 0.00092169 5.32611475 + layer.19.0 3.23006092 23.09737315 + layer.19.1 3.23257961 23.17206796 + layer.29.0 4.28548854 210.26261375 + layer.29.1 4.27808990 203.40670702 + layer.39.0 10.57841825 15455.56321092 + layer.39.1 20.33118703 15737.20766981 + ------------------------------------------------------------------------------------- + TOTAL 5.75625939 3957.94394208 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 656940 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 3957.943942 +---------------------- ---------------------------------------------------------- +Time: 64.823s Load: 1.169s, Pack+Encode: 32.755s, Decode+Unpack: 30.900s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3957.9439 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,708B, BPFP=0.0265 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,112B, BPFP=0.0261 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,032B, BPFP=0.0375 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,628B, BPFP=0.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,884B, BPFP=0.0659 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,068B, BPFP=0.0667 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 107,488B, BPFP=0.0682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 109,324B, BPFP=0.0694 +⌛️ [2/4] FRONTEND: Frontend time: 32.405s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.082s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14443954 5.39879994 + layer.9.1 0.14435121 5.23748623 + layer.19.0 0.03807715 23.52791122 + layer.19.1 0.03781311 23.33569883 + layer.29.0 0.10781899 206.69233425 + layer.29.1 0.10618912 203.93847498 + layer.39.0 9.30898666 14916.28469288 + layer.39.1 9.83625107 14908.88917777 + ------------------------------------------------------------------------------------- + TOTAL 2.46549086 3786.66307201 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 628244 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 3786.663072 +---------------------- ---------------------------------------------------------- +Time: 64.574s Load: 1.088s, Pack+Encode: 32.405s, Decode+Unpack: 31.082s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3786.6631 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.159s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,808B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,004B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 55,872B, BPFP=0.0355 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 56,580B, BPFP=0.0359 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,416B, BPFP=0.0720 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,000B, BPFP=0.0711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 120,508B, BPFP=0.0765 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 118,696B, BPFP=0.0753 +⌛️ [2/4] FRONTEND: Frontend time: 32.469s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.397s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14543928 5.30193681 + layer.9.1 0.14562574 5.42278142 + layer.19.0 0.11552505 24.31910140 + layer.19.1 0.12052174 24.61076891 + layer.29.0 0.10841144 225.07320442 + layer.29.1 0.10845811 226.11520962 + layer.39.0 9.17501701 15552.80727982 + layer.39.1 9.20635778 15489.83425414 + ------------------------------------------------------------------------------------- + TOTAL 2.39066952 3944.18556707 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 654884 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 3944.185567 +---------------------- ---------------------------------------------------------- +Time: 65.026s Load: 1.159s, Pack+Encode: 32.469s, Decode+Unpack: 31.397s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3944.1856 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.164s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,628B, BPFP=0.0258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,580B, BPFP=0.0264 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,428B, BPFP=0.0384 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,612B, BPFP=0.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 111,156B, BPFP=0.0706 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,128B, BPFP=0.0712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 118,240B, BPFP=0.0751 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 121,612B, BPFP=0.0772 +⌛️ [2/4] FRONTEND: Frontend time: 32.837s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.168s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.78226592 5.28949127 + layer.9.1 2.78427046 5.25120444 + layer.19.0 3.22580366 23.55947605 + layer.19.1 3.22969594 23.18476296 + layer.29.0 4.29525448 212.15774293 + layer.29.1 0.11349234 222.94148115 + layer.39.0 8.89338553 14669.47676308 + layer.39.1 8.88767087 14912.50178746 + ------------------------------------------------------------------------------------- + TOTAL 4.27647990 3759.29533867 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 666384 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 3759.295339 +---------------------- ---------------------------------------------------------- +Time: 65.168s Load: 1.164s, Pack+Encode: 32.837s, Decode+Unpack: 31.168s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3759.2953 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.150s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,480B, BPFP=0.0263 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,376B, BPFP=0.0269 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,188B, BPFP=0.0414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,712B, BPFP=0.0404 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,432B, BPFP=0.0720 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,664B, BPFP=0.0721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 114,460B, BPFP=0.0727 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 114,288B, BPFP=0.0725 +⌛️ [2/4] FRONTEND: Frontend time: 32.051s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.239s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14514284 5.23974149 + layer.9.1 0.14518188 5.29145265 + layer.19.0 0.04057091 23.09960239 + layer.19.1 0.04041447 23.80020109 + layer.29.0 4.25641542 202.84170864 + layer.29.1 4.26613502 208.41115535 + layer.39.0 12.58558458 14978.12934677 + layer.39.1 8.96866240 15022.73643159 + ------------------------------------------------------------------------------------- + TOTAL 3.80601344 3808.69370500 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 668600 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 3808.693705 +---------------------- ---------------------------------------------------------- +Time: 64.439s Load: 1.150s, Pack+Encode: 32.051s, Decode+Unpack: 31.239s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3808.6937 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,724B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,692B, BPFP=0.0252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,564B, BPFP=0.0384 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,140B, BPFP=0.0369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,440B, BPFP=0.0663 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,952B, BPFP=0.0660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 104,656B, BPFP=0.0664 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 106,488B, BPFP=0.0676 +⌛️ [2/4] FRONTEND: Frontend time: 32.040s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.262s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67015388 5.25544615 + layer.9.1 0.00076871 5.23584159 + layer.19.0 3.22151687 22.57728713 + layer.19.1 3.22388957 22.89115819 + layer.29.0 4.24084786 188.56101722 + layer.29.1 4.24602234 187.91269906 + layer.39.0 7.87160790 15716.25869353 + layer.39.1 9.85764150 15703.81670458 + ------------------------------------------------------------------------------------- + TOTAL 4.41655608 3981.56360593 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 617656 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 3981.563606 +---------------------- ---------------------------------------------------------- +Time: 64.477s Load: 1.175s, Pack+Encode: 32.040s, Decode+Unpack: 31.262s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3981.5636 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.065s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,796B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,016B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,952B, BPFP=0.0406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,912B, BPFP=0.0406 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 151,008B, BPFP=0.0959 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 147,436B, BPFP=0.0936 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,420B, BPFP=0.1145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 179,172B, BPFP=0.1137 +⌛️ [2/4] FRONTEND: Frontend time: 32.808s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00068237 5.32857313 + layer.9.1 0.00070576 5.18883710 + layer.19.0 0.00823322 23.63975666 + layer.19.1 0.08594799 24.07753595 + layer.29.0 0.12200666 233.17831898 + layer.29.1 0.12451052 239.43780468 + layer.39.0 55.99513528 16106.32174196 + layer.39.1 28.81185256 16472.98797530 + ------------------------------------------------------------------------------------- + TOTAL 10.64363429 4138.77006797 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 863712 +BPFP 0.0685 bits/point +EBPFP 0.0685 equivalent bits/point +MSE 4138.770068 +---------------------- ---------------------------------------------------------- +Time: 65.480s Load: 1.065s, Pack+Encode: 32.808s, Decode+Unpack: 31.608s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4138.7701 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.115s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,244B, BPFP=0.0255 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,872B, BPFP=0.0259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,052B, BPFP=0.0400 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,840B, BPFP=0.0399 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 119,568B, BPFP=0.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 119,576B, BPFP=0.0759 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 119,668B, BPFP=0.0760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 123,032B, BPFP=0.0781 +⌛️ [2/4] FRONTEND: Frontend time: 31.396s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.415s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14556937 5.30394992 + layer.9.1 0.03327741 5.21257896 + layer.19.0 0.11590617 24.19247898 + layer.19.1 0.11733878 23.81125081 + layer.29.0 0.11334742 212.65343679 + layer.29.1 4.29039579 213.27650715 + layer.39.0 9.10722066 15811.73090673 + layer.39.1 44.52401893 16054.88982775 + ------------------------------------------------------------------------------------- + TOTAL 7.30588432 4043.88386714 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 688852 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 4043.883867 +---------------------- ---------------------------------------------------------- +Time: 63.926s Load: 1.115s, Pack+Encode: 31.396s, Decode+Unpack: 31.415s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4043.8839 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.034s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,516B, BPFP=0.0251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,740B, BPFP=0.0252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,100B, BPFP=0.0401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,016B, BPFP=0.0400 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,924B, BPFP=0.0787 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,888B, BPFP=0.0799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 125,152B, BPFP=0.0794 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 128,848B, BPFP=0.0818 +⌛️ [2/4] FRONTEND: Frontend time: 31.903s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.223s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11318631 5.10985995 + layer.9.1 0.11319129 5.10334678 + layer.19.0 0.00665199 21.54898745 + layer.19.1 0.00853768 21.60917950 + layer.29.0 4.27225940 227.07036074 + layer.29.1 4.27324961 215.83443695 + layer.39.0 14.80262837 14949.31946701 + layer.39.1 16.56649765 13627.92070198 + ------------------------------------------------------------------------------------- + TOTAL 5.01952529 3634.18954255 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 709184 +BPFP 0.0563 bits/point +EBPFP 0.0563 equivalent bits/point +MSE 3634.189543 +---------------------- ---------------------------------------------------------- +Time: 64.160s Load: 1.034s, Pack+Encode: 31.903s, Decode+Unpack: 31.223s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3634.1895 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.030s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,472B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,628B, BPFP=0.0245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,980B, BPFP=0.0381 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,016B, BPFP=0.0375 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 121,776B, BPFP=0.0773 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 120,892B, BPFP=0.0767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 131,668B, BPFP=0.0836 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 132,348B, BPFP=0.0840 +⌛️ [2/4] FRONTEND: Frontend time: 32.180s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.375s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00065117 5.24897805 + layer.9.1 0.00066201 5.27383295 + layer.19.0 0.00984582 23.95544311 + layer.19.1 0.01156107 23.54757322 + layer.29.0 4.26547583 228.76665583 + layer.29.1 4.26296603 226.27031199 + layer.39.0 11.21169412 15228.74618135 + layer.39.1 9.31977106 15415.08482288 + ------------------------------------------------------------------------------------- + TOTAL 3.63532839 3894.61172492 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 702780 +BPFP 0.0558 bits/point +EBPFP 0.0558 equivalent bits/point +MSE 3894.611725 +---------------------- ---------------------------------------------------------- +Time: 64.585s Load: 1.030s, Pack+Encode: 32.180s, Decode+Unpack: 31.375s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3894.6117 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.108s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,216B, BPFP=0.0243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,008B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,256B, BPFP=0.0376 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,328B, BPFP=0.0370 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 116,856B, BPFP=0.0742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 116,088B, BPFP=0.0737 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 116,356B, BPFP=0.0739 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 114,644B, BPFP=0.0728 +⌛️ [2/4] FRONTEND: Frontend time: 32.082s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.221s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00085355 5.37359657 + layer.9.1 0.00085581 5.17543785 + layer.19.0 0.00808159 22.81949240 + layer.19.1 0.00635426 22.55728236 + layer.29.0 4.24551200 218.63050455 + layer.29.1 4.24803037 215.03365697 + layer.39.0 9.19283951 14689.39486513 + layer.39.1 9.46657027 14495.57101072 + ------------------------------------------------------------------------------------- + TOTAL 3.39613717 3709.31948082 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 657752 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 3709.319481 +---------------------- ---------------------------------------------------------- +Time: 64.411s Load: 1.108s, Pack+Encode: 32.082s, Decode+Unpack: 31.221s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3709.3195 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,516B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,900B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,932B, BPFP=0.0368 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,096B, BPFP=0.0369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 110,476B, BPFP=0.0701 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,120B, BPFP=0.0712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 113,028B, BPFP=0.0717 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 119,032B, BPFP=0.0756 +⌛️ [2/4] FRONTEND: Frontend time: 32.753s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.180s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67056234 5.56589401 + layer.9.1 2.67147828 5.46371897 + layer.19.0 0.00618387 24.48028721 + layer.19.1 0.08383032 24.14667543 + layer.29.0 4.28489822 208.23155671 + layer.29.1 4.28470970 210.70458239 + layer.39.0 10.15376305 15058.04614885 + layer.39.1 8.47863686 15277.19207020 + ------------------------------------------------------------------------------------- + TOTAL 4.07925783 3851.72886672 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 647100 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 3851.728867 +---------------------- ---------------------------------------------------------- +Time: 64.986s Load: 1.053s, Pack+Encode: 32.753s, Decode+Unpack: 31.180s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3851.7289 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.096s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,624B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,324B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,508B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,456B, BPFP=0.0396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 129,072B, BPFP=0.0819 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 129,632B, BPFP=0.0823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 135,056B, BPFP=0.0857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 135,752B, BPFP=0.0862 +⌛️ [2/4] FRONTEND: Frontend time: 33.006s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.382s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67132404 5.44889439 + layer.9.1 2.67117709 5.37778465 + layer.19.0 0.00597838 24.50884333 + layer.19.1 0.00605309 24.38638487 + layer.29.0 4.29273040 228.95159652 + layer.29.1 4.29206328 235.28910058 + layer.39.0 9.96127074 15507.74780630 + layer.39.1 10.21295854 14961.26486838 + ------------------------------------------------------------------------------------- + TOTAL 4.26419445 3874.12190988 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 730424 +BPFP 0.0580 bits/point +EBPFP 0.0580 equivalent bits/point +MSE 3874.121910 +---------------------- ---------------------------------------------------------- +Time: 65.484s Load: 1.096s, Pack+Encode: 33.006s, Decode+Unpack: 31.382s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3874.1219 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.101s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,728B, BPFP=0.0259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,648B, BPFP=0.0258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,268B, BPFP=0.0395 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,516B, BPFP=0.0397 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,272B, BPFP=0.0713 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,700B, BPFP=0.0715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 115,988B, BPFP=0.0736 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 117,288B, BPFP=0.0744 +⌛️ [2/4] FRONTEND: Frontend time: 32.017s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.427s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568686 5.23607835 + layer.9.1 0.14558674 5.25110161 + layer.19.0 0.00960369 24.02968598 + layer.19.1 0.03847206 24.07067304 + layer.29.0 4.24438723 202.84889909 + layer.29.1 4.24578970 199.83242606 + layer.39.0 9.23757985 14849.72115697 + layer.39.1 9.43674592 14859.52551186 + ------------------------------------------------------------------------------------- + TOTAL 3.43798151 3771.31444162 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 664408 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 3771.314442 +---------------------- ---------------------------------------------------------- +Time: 64.544s Load: 1.101s, Pack+Encode: 32.017s, Decode+Unpack: 31.427s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3771.3144 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.188s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,536B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,288B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,092B, BPFP=0.0400 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,584B, BPFP=0.0404 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 127,424B, BPFP=0.0809 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,556B, BPFP=0.0797 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,428B, BPFP=0.0898 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 147,672B, BPFP=0.0937 +⌛️ [2/4] FRONTEND: Frontend time: 32.156s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.337s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11307967 5.36124942 + layer.9.1 0.00073224 5.27832825 + layer.19.0 0.08207503 25.17102697 + layer.19.1 0.08214869 24.41362325 + layer.29.0 4.26728487 226.69044524 + layer.29.1 4.26774951 238.23935652 + layer.39.0 12.81553410 15616.65128372 + layer.39.1 23.05196315 15587.82060448 + ------------------------------------------------------------------------------------- + TOTAL 5.58507091 3966.20323973 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 745580 +BPFP 0.0592 bits/point +EBPFP 0.0592 equivalent bits/point +MSE 3966.203240 +---------------------- ---------------------------------------------------------- +Time: 64.680s Load: 1.188s, Pack+Encode: 32.156s, Decode+Unpack: 31.337s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3966.2032 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,440B, BPFP=0.0269 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,596B, BPFP=0.0264 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 73,236B, BPFP=0.0465 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 72,264B, BPFP=0.0459 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 142,596B, BPFP=0.0905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 140,924B, BPFP=0.0895 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 129,576B, BPFP=0.0822 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 128,284B, BPFP=0.0814 +⌛️ [2/4] FRONTEND: Frontend time: 32.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519844 5.29117653 + layer.9.1 0.14499054 5.16179898 + layer.19.0 0.12156012 18.60099757 + layer.19.1 0.12030756 18.83050531 + layer.29.0 0.12020218 203.71441339 + layer.29.1 0.12115470 219.73998619 + layer.39.0 8.85439666 15507.04452389 + layer.39.1 8.75438231 15140.52518687 + ------------------------------------------------------------------------------------- + TOTAL 2.29777406 3889.86357359 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 770916 +BPFP 0.0612 bits/point +EBPFP 0.0612 equivalent bits/point +MSE 3889.863574 +---------------------- ---------------------------------------------------------- +Time: 64.748s Load: 1.221s, Pack+Encode: 32.523s, Decode+Unpack: 31.004s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3889.8636 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.194s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,680B, BPFP=0.0277 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 44,116B, BPFP=0.0280 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 85,884B, BPFP=0.0545 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 87,092B, BPFP=0.0553 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 170,652B, BPFP=0.1083 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 168,252B, BPFP=0.1068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 127,664B, BPFP=0.0810 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 126,624B, BPFP=0.0804 +⌛️ [2/4] FRONTEND: Frontend time: 32.440s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.347s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14541288 5.22568178 + layer.9.1 0.14479464 5.39565793 + layer.19.0 0.11855170 14.59575581 + layer.19.1 0.11778439 13.62793635 + layer.29.0 0.12648388 183.20829948 + layer.29.1 0.12520221 197.38659815 + layer.39.0 8.37129624 16224.82937927 + layer.39.1 8.45478741 15271.92720182 + ------------------------------------------------------------------------------------- + TOTAL 2.20053917 3989.52456382 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 853964 +BPFP 0.0678 bits/point +EBPFP 0.0678 equivalent bits/point +MSE 3989.524564 +---------------------- ---------------------------------------------------------- +Time: 64.982s Load: 1.194s, Pack+Encode: 32.440s, Decode+Unpack: 31.347s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3989.5246 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.168s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,336B, BPFP=0.0243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,148B, BPFP=0.0242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,536B, BPFP=0.0403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,844B, BPFP=0.0405 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 117,316B, BPFP=0.0745 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,556B, BPFP=0.0727 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 126,180B, BPFP=0.0801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 127,808B, BPFP=0.0811 +⌛️ [2/4] FRONTEND: Frontend time: 32.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.250s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14458197 5.45106079 + layer.9.1 0.14461228 5.28210342 + layer.19.0 0.12127609 16.15190450 + layer.19.1 0.12505172 20.70105825 + layer.29.0 0.11568762 228.84028681 + layer.29.1 0.11796058 244.27151040 + layer.39.0 8.63782956 16612.52518687 + layer.39.1 8.69862780 15549.45856354 + ------------------------------------------------------------------------------------- + TOTAL 2.26320345 4085.33520932 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 689724 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 4085.335209 +---------------------- ---------------------------------------------------------- +Time: 64.969s Load: 1.168s, Pack+Encode: 32.551s, Decode+Unpack: 31.250s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4085.3352 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,116B, BPFP=0.0242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,236B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 70,560B, BPFP=0.0448 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,860B, BPFP=0.0443 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 124,060B, BPFP=0.0787 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 123,628B, BPFP=0.0785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 128,560B, BPFP=0.0816 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 128,192B, BPFP=0.0814 +⌛️ [2/4] FRONTEND: Frontend time: 32.536s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14478087 5.35419925 + layer.9.1 0.14472154 5.45125185 + layer.19.0 0.13423899 14.63607511 + layer.19.1 0.13534726 16.27668107 + layer.29.0 0.11251127 210.23795499 + layer.29.1 0.11242151 212.56542493 + layer.39.0 10.58490794 15992.40428989 + layer.39.1 8.80008176 15549.15957101 + ------------------------------------------------------------------------------------- + TOTAL 2.52112639 4000.76068101 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 721212 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 4000.760681 +---------------------- ---------------------------------------------------------- +Time: 65.162s Load: 1.176s, Pack+Encode: 32.536s, Decode+Unpack: 31.449s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4000.7607 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,512B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,840B, BPFP=0.0253 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,164B, BPFP=0.0382 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,992B, BPFP=0.0381 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,528B, BPFP=0.0721 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,324B, BPFP=0.0719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 139,664B, BPFP=0.0887 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 140,232B, BPFP=0.0890 +⌛️ [2/4] FRONTEND: Frontend time: 32.713s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.371s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14590075 5.07833320 + layer.9.1 0.14620647 5.09934976 + layer.19.0 0.11628058 22.47642793 + layer.19.1 0.11601873 23.20199007 + layer.29.0 0.11558260 215.39118866 + layer.29.1 0.11828149 215.21404777 + layer.39.0 28.43028163 16963.47871303 + layer.39.1 24.81181701 17083.73740656 + ------------------------------------------------------------------------------------- + TOTAL 6.75004616 4316.70968212 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 707256 +BPFP 0.0561 bits/point +EBPFP 0.0561 equivalent bits/point +MSE 4316.709682 +---------------------- ---------------------------------------------------------- +Time: 65.308s Load: 1.223s, Pack+Encode: 32.713s, Decode+Unpack: 31.371s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4316.7097 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.149s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,220B, BPFP=0.0236 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,728B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,824B, BPFP=0.0380 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,312B, BPFP=0.0389 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,844B, BPFP=0.0665 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,648B, BPFP=0.0671 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 131,676B, BPFP=0.0836 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 126,388B, BPFP=0.0802 +⌛️ [2/4] FRONTEND: Frontend time: 32.460s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.265s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11529700 5.21749161 + layer.9.1 0.14629077 5.37150824 + layer.19.0 0.09721754 22.89827754 + layer.19.1 0.12446257 22.44760776 + layer.29.0 4.28687864 218.44901690 + layer.29.1 4.28715508 215.08646815 + layer.39.0 11.34089363 17304.84887878 + layer.39.1 19.75513766 17415.23821904 + ------------------------------------------------------------------------------------- + TOTAL 5.01916661 4401.19468350 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 664640 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 4401.194684 +---------------------- ---------------------------------------------------------- +Time: 64.875s Load: 1.149s, Pack+Encode: 32.460s, Decode+Unpack: 31.265s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4401.1947 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,580B, BPFP=0.0251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,276B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,576B, BPFP=0.0391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,220B, BPFP=0.0395 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,152B, BPFP=0.0718 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,768B, BPFP=0.0722 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 156,864B, BPFP=0.0996 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 166,424B, BPFP=0.1056 +⌛️ [2/4] FRONTEND: Frontend time: 32.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.421s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14538718 5.37036887 + layer.9.1 0.14538559 5.32434316 + layer.19.0 0.11434236 25.14171169 + layer.19.1 0.11406084 25.51432757 + layer.29.0 0.11219077 221.17372847 + layer.29.1 0.11281304 225.83908840 + layer.39.0 79.88316542 18806.13324667 + layer.39.1 46.71980622 18912.73838154 + ------------------------------------------------------------------------------------- + TOTAL 15.91839393 4778.40439955 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 752860 +BPFP 0.0597 bits/point +EBPFP 0.0597 equivalent bits/point +MSE 4778.404400 +---------------------- ---------------------------------------------------------- +Time: 65.145s Load: 1.173s, Pack+Encode: 32.551s, Decode+Unpack: 31.421s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4778.4044 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,916B, BPFP=0.0253 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,672B, BPFP=0.0258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,124B, BPFP=0.0394 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,360B, BPFP=0.0396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,020B, BPFP=0.0711 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,316B, BPFP=0.0713 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 154,412B, BPFP=0.0980 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 154,564B, BPFP=0.0981 +⌛️ [2/4] FRONTEND: Frontend time: 32.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.087s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14512126 5.24486519 + layer.9.1 0.14517278 5.24166383 + layer.19.0 0.11689420 23.63889086 + layer.19.1 0.12099910 23.14655864 + layer.29.0 0.11847120 224.01364966 + layer.29.1 0.12399357 229.14982125 + layer.39.0 75.86630139 18221.23886903 + layer.39.1 56.61936342 18185.83555411 + ------------------------------------------------------------------------------------- + TOTAL 16.65703962 4614.68873407 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 738384 +BPFP 0.0586 bits/point +EBPFP 0.0586 equivalent bits/point +MSE 4614.688734 +---------------------- ---------------------------------------------------------- +Time: 64.470s Load: 1.172s, Pack+Encode: 32.210s, Decode+Unpack: 31.087s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4614.6887 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,912B, BPFP=0.0234 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,048B, BPFP=0.0235 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 54,988B, BPFP=0.0349 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 54,644B, BPFP=0.0347 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 108,928B, BPFP=0.0691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 108,384B, BPFP=0.0688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,720B, BPFP=0.1052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 165,200B, BPFP=0.1049 +⌛️ [2/4] FRONTEND: Frontend time: 32.237s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.872s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488649 5.37212268 + layer.9.1 0.14606862 5.27267612 + layer.19.0 0.08767178 25.50226225 + layer.19.1 0.11443626 25.39553035 + layer.29.0 0.10933029 222.25475301 + layer.29.1 0.10817130 218.94150146 + layer.39.0 52.66717785 18448.26519337 + layer.39.1 62.91127214 18169.45076373 + ------------------------------------------------------------------------------------- + TOTAL 14.53237684 4640.05685037 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 731824 +BPFP 0.0581 bits/point +EBPFP 0.0581 equivalent bits/point +MSE 4640.056850 +---------------------- ---------------------------------------------------------- +Time: 64.317s Load: 1.209s, Pack+Encode: 32.237s, Decode+Unpack: 30.872s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4640.0569 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,728B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,868B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 55,080B, BPFP=0.0350 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 54,776B, BPFP=0.0348 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,716B, BPFP=0.0627 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,624B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 139,332B, BPFP=0.0884 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 138,984B, BPFP=0.0882 +⌛️ [2/4] FRONTEND: Frontend time: 32.744s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.422s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509552 5.37756439 + layer.9.1 0.14520687 5.43753237 + layer.19.0 0.12118574 24.86050485 + layer.19.1 0.11709642 25.12357562 + layer.29.0 0.10963326 213.29560042 + layer.29.1 0.10842036 215.08474163 + layer.39.0 53.79489966 17036.34189145 + layer.39.1 62.27410526 17753.37926552 + ------------------------------------------------------------------------------------- + TOTAL 14.60195539 4409.86258453 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 663108 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4409.862585 +---------------------- ---------------------------------------------------------- +Time: 65.335s Load: 1.169s, Pack+Encode: 32.744s, Decode+Unpack: 31.422s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4409.8626 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.194s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,260B, BPFP=0.0256 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,992B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,668B, BPFP=0.0372 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,336B, BPFP=0.0377 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 106,156B, BPFP=0.0674 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,584B, BPFP=0.0670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 151,520B, BPFP=0.0962 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 151,476B, BPFP=0.0961 +⌛️ [2/4] FRONTEND: Frontend time: 32.312s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14577075 5.30653272 + layer.9.1 0.14541274 5.33219565 + layer.19.0 0.13069581 21.35157773 + layer.19.1 0.13545482 20.93629778 + layer.29.0 0.11331055 233.21394621 + layer.29.1 0.11244963 230.78451007 + layer.39.0 32.27446072 18180.81767956 + layer.39.1 16.59366367 18221.44816380 + ------------------------------------------------------------------------------------- + TOTAL 6.20640234 4614.89886294 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 712992 +BPFP 0.0566 bits/point +EBPFP 0.0566 equivalent bits/point +MSE 4614.898863 +---------------------- ---------------------------------------------------------- +Time: 64.960s Load: 1.194s, Pack+Encode: 32.312s, Decode+Unpack: 31.453s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4614.8989 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.180s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,000B, BPFP=0.0241 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,004B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,876B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,932B, BPFP=0.0374 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 114,012B, BPFP=0.0724 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,088B, BPFP=0.0724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 140,888B, BPFP=0.0894 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 136,540B, BPFP=0.0867 +⌛️ [2/4] FRONTEND: Frontend time: 32.329s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.263s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585920 5.23472856 + layer.9.1 0.14576220 5.45048063 + layer.19.0 0.12270736 24.58684138 + layer.19.1 0.12453605 24.27348066 + layer.29.0 0.11393550 214.20927446 + layer.29.1 0.11678154 219.61431589 + layer.39.0 53.83016636 18069.19337017 + layer.39.1 40.65720720 17932.21189470 + ------------------------------------------------------------------------------------- + TOTAL 11.90711942 4561.84679831 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 699340 +BPFP 0.0555 bits/point +EBPFP 0.0555 equivalent bits/point +MSE 4561.846798 +---------------------- ---------------------------------------------------------- +Time: 64.772s Load: 1.180s, Pack+Encode: 32.329s, Decode+Unpack: 31.263s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4561.8468 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.149s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,696B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,856B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,360B, BPFP=0.0428 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,256B, BPFP=0.0440 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 127,752B, BPFP=0.0811 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,288B, BPFP=0.0795 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 139,196B, BPFP=0.0884 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 138,952B, BPFP=0.0882 +⌛️ [2/4] FRONTEND: Frontend time: 32.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568599 5.44825330 + layer.9.1 0.03329684 5.44767821 + layer.19.0 0.11848472 23.25241205 + layer.19.1 0.11973745 22.38467866 + layer.29.0 0.10886538 232.16412090 + layer.29.1 0.10946879 231.69922002 + layer.39.0 14.08931437 17180.85147871 + layer.39.1 9.95616799 17608.32499188 + ------------------------------------------------------------------------------------- + TOTAL 3.08512769 4413.69660422 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 745356 +BPFP 0.0591 bits/point +EBPFP 0.0591 equivalent bits/point +MSE 4413.696604 +---------------------- ---------------------------------------------------------- +Time: 65.236s Load: 1.149s, Pack+Encode: 32.580s, Decode+Unpack: 31.507s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4413.6966 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.231s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,108B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,268B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,900B, BPFP=0.0418 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,972B, BPFP=0.0412 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,120B, BPFP=0.0782 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 124,024B, BPFP=0.0787 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,864B, BPFP=0.0831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 130,620B, BPFP=0.0829 +⌛️ [2/4] FRONTEND: Frontend time: 32.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.324s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14493624 5.09860234 + layer.9.1 0.14482686 5.30922310 + layer.19.0 0.11946148 23.04503423 + layer.19.1 0.12828579 22.22404889 + layer.29.0 0.10467725 209.37138447 + layer.29.1 0.10613328 212.51147628 + layer.39.0 22.00188902 16791.37601560 + layer.39.1 19.26198661 16717.88365291 + ------------------------------------------------------------------------------------- + TOTAL 5.25152457 4248.35242973 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 717876 +BPFP 0.0570 bits/point +EBPFP 0.0570 equivalent bits/point +MSE 4248.352430 +---------------------- ---------------------------------------------------------- +Time: 64.841s Load: 1.231s, Pack+Encode: 32.286s, Decode+Unpack: 31.324s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4248.3524 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.234s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,120B, BPFP=0.0255 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,096B, BPFP=0.0255 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,820B, BPFP=0.0430 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,712B, BPFP=0.0430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 132,984B, BPFP=0.0844 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 129,648B, BPFP=0.0823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 135,656B, BPFP=0.0861 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 138,448B, BPFP=0.0879 +⌛️ [2/4] FRONTEND: Frontend time: 32.730s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.094s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14475438 5.32234433 + layer.9.1 0.14492096 5.12475975 + layer.19.0 0.11744098 22.37307290 + layer.19.1 0.11578254 21.90125579 + layer.29.0 0.11402616 191.62351722 + layer.29.1 0.11062706 198.79545824 + layer.39.0 28.92800668 16613.85765356 + layer.39.1 10.80449708 16839.17322067 + ------------------------------------------------------------------------------------- + TOTAL 5.06000698 4237.27141031 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 752484 +BPFP 0.0597 bits/point +EBPFP 0.0597 equivalent bits/point +MSE 4237.271410 +---------------------- ---------------------------------------------------------- +Time: 65.057s Load: 1.234s, Pack+Encode: 32.730s, Decode+Unpack: 31.094s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4237.2714 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,408B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,904B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,892B, BPFP=0.0399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,308B, BPFP=0.0402 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,908B, BPFP=0.0736 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 118,448B, BPFP=0.0752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 113,036B, BPFP=0.0717 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 113,032B, BPFP=0.0717 +⌛️ [2/4] FRONTEND: Frontend time: 32.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.237s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14546214 5.34175371 + layer.9.1 0.14553630 5.07394168 + layer.19.0 0.04765745 24.79010095 + layer.19.1 0.04191649 25.01409144 + layer.29.0 0.16505912 217.05512675 + layer.29.1 0.15755973 216.87185164 + layer.39.0 42.51041751 16197.73285668 + layer.39.1 31.38856333 16162.18914527 + ------------------------------------------------------------------------------------- + TOTAL 9.32527151 4106.75860852 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 664936 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 4106.758609 +---------------------- ---------------------------------------------------------- +Time: 65.092s Load: 1.215s, Pack+Encode: 32.640s, Decode+Unpack: 31.237s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4106.7586 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.093s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,724B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,744B, BPFP=0.0246 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,456B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,468B, BPFP=0.0384 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,728B, BPFP=0.0785 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,028B, BPFP=0.0768 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 146,576B, BPFP=0.0930 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 145,984B, BPFP=0.0927 +⌛️ [2/4] FRONTEND: Frontend time: 32.897s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.146s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03343166 5.42866174 + layer.9.1 0.03311388 5.37091475 + layer.19.0 0.03842411 24.37169930 + layer.19.1 0.03806642 24.22311962 + layer.29.0 4.26870163 225.74297205 + layer.29.1 4.26552788 222.95870572 + layer.39.0 33.95300821 16177.54956126 + layer.39.1 48.19954501 15790.93662658 + ------------------------------------------------------------------------------------- + TOTAL 11.35372735 4059.57278263 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 736708 +BPFP 0.0585 bits/point +EBPFP 0.0585 equivalent bits/point +MSE 4059.572783 +---------------------- ---------------------------------------------------------- +Time: 65.137s Load: 1.093s, Pack+Encode: 32.897s, Decode+Unpack: 31.146s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4059.5728 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.064s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,424B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,044B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,588B, BPFP=0.0404 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,252B, BPFP=0.0401 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,144B, BPFP=0.0731 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,716B, BPFP=0.0735 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 138,856B, BPFP=0.0881 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 141,512B, BPFP=0.0898 +⌛️ [2/4] FRONTEND: Frontend time: 32.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.348s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549638 5.46813874 + layer.9.1 0.14520178 5.23036878 + layer.19.0 0.11487435 24.55263091 + layer.19.1 0.11481158 24.70010867 + layer.29.0 0.10827909 220.11522993 + layer.29.1 0.10618535 225.52350097 + layer.39.0 9.83978281 16069.93305167 + layer.39.1 9.67554703 16121.50536237 + ------------------------------------------------------------------------------------- + TOTAL 2.53127230 4087.12854900 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 718536 +BPFP 0.0570 bits/point +EBPFP 0.0570 equivalent bits/point +MSE 4087.128549 +---------------------- ---------------------------------------------------------- +Time: 64.975s Load: 1.064s, Pack+Encode: 32.564s, Decode+Unpack: 31.348s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4087.1285 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.999s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,876B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,988B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 56,988B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,340B, BPFP=0.0370 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,648B, BPFP=0.0734 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,776B, BPFP=0.0735 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 144,228B, BPFP=0.0915 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 146,224B, BPFP=0.0928 +⌛️ [2/4] FRONTEND: Frontend time: 31.746s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092318 5.38507348 + layer.9.1 0.00095285 5.48165256 + layer.19.0 0.08568402 25.15429497 + layer.19.1 0.08404610 24.85858537 + layer.29.0 0.12100375 222.94690445 + layer.29.1 0.12795564 226.13544036 + layer.39.0 12.85620633 15662.92102697 + layer.39.1 12.98640239 15648.04549886 + ------------------------------------------------------------------------------------- + TOTAL 3.28289678 3977.61605963 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 713068 +BPFP 0.0566 bits/point +EBPFP 0.0566 equivalent bits/point +MSE 3977.616060 +---------------------- ---------------------------------------------------------- +Time: 63.834s Load: 0.999s, Pack+Encode: 31.746s, Decode+Unpack: 31.089s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3977.6161 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.030s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,660B, BPFP=0.0233 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,192B, BPFP=0.0236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 52,316B, BPFP=0.0332 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 53,676B, BPFP=0.0341 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 106,680B, BPFP=0.0677 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 106,256B, BPFP=0.0674 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 106,956B, BPFP=0.0679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 110,288B, BPFP=0.0700 +⌛️ [2/4] FRONTEND: Frontend time: 31.828s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00113376 5.53517275 + layer.9.1 0.00100095 5.54561121 + layer.19.0 0.00983371 24.79733456 + layer.19.1 0.00806405 24.91717023 + layer.29.0 4.28365570 223.54694101 + layer.29.1 4.28597952 218.92842054 + layer.39.0 8.41906814 15541.03087423 + layer.39.1 8.59662605 15099.15632109 + ------------------------------------------------------------------------------------- + TOTAL 3.20067024 3892.93223070 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 610024 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 3892.932231 +---------------------- ---------------------------------------------------------- +Time: 63.884s Load: 1.030s, Pack+Encode: 31.828s, Decode+Unpack: 31.026s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3892.9322 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,612B, BPFP=0.0239 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,636B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 55,168B, BPFP=0.0350 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 55,116B, BPFP=0.0350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 107,404B, BPFP=0.0682 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 110,056B, BPFP=0.0699 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,512B, BPFP=0.0708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 109,680B, BPFP=0.0696 +⌛️ [2/4] FRONTEND: Frontend time: 32.388s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.299s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550294 5.39300531 + layer.9.1 0.14526658 5.29388787 + layer.19.0 0.11599200 24.58293894 + layer.19.1 0.11361485 24.61700215 + layer.29.0 4.26439454 211.92297693 + layer.29.1 4.25587461 199.70435895 + layer.39.0 8.37236706 15773.45986350 + layer.39.1 8.35116642 15373.11667208 + ------------------------------------------------------------------------------------- + TOTAL 3.22052237 3952.26133822 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 624184 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 3952.261338 +---------------------- ---------------------------------------------------------- +Time: 64.862s Load: 1.175s, Pack+Encode: 32.388s, Decode+Unpack: 31.299s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3952.2613 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,852B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,844B, BPFP=0.0240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,456B, BPFP=0.0377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,576B, BPFP=0.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,040B, BPFP=0.0711 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 111,472B, BPFP=0.0708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 126,300B, BPFP=0.0802 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 130,592B, BPFP=0.0829 +⌛️ [2/4] FRONTEND: Frontend time: 32.994s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00080404 5.42081179 + layer.9.1 0.00082438 5.30257727 + layer.19.0 0.00843097 24.75314328 + layer.19.1 0.00674472 24.49978926 + layer.29.0 4.27713270 202.27335879 + layer.29.1 4.27133426 205.68695158 + layer.39.0 22.97048921 15456.99707507 + layer.39.1 18.06488920 15542.93922652 + ------------------------------------------------------------------------------------- + TOTAL 6.20008118 3933.48411670 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 676132 +BPFP 0.0536 bits/point +EBPFP 0.0536 equivalent bits/point +MSE 3933.484117 +---------------------- ---------------------------------------------------------- +Time: 65.267s Load: 1.213s, Pack+Encode: 32.994s, Decode+Unpack: 31.060s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3933.4841 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.104s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,184B, BPFP=0.0242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,864B, BPFP=0.0240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,948B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,412B, BPFP=0.0383 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,800B, BPFP=0.0716 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,440B, BPFP=0.0714 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 134,704B, BPFP=0.0855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 136,372B, BPFP=0.0866 +⌛️ [2/4] FRONTEND: Frontend time: 33.174s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14506060 5.40412549 + layer.9.1 0.14523201 5.42506970 + layer.19.0 0.04621643 23.01901202 + layer.19.1 0.04629335 24.28776253 + layer.29.0 4.27940669 218.12768118 + layer.29.1 4.27759670 215.56377966 + layer.39.0 19.91382637 15652.04419890 + layer.39.1 24.01088215 15784.68248294 + ------------------------------------------------------------------------------------- + TOTAL 6.60806429 3991.06926405 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 691724 +BPFP 0.0549 bits/point +EBPFP 0.0549 equivalent bits/point +MSE 3991.069264 +---------------------- ---------------------------------------------------------- +Time: 65.762s Load: 1.104s, Pack+Encode: 33.174s, Decode+Unpack: 31.485s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3991.0693 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.020s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,532B, BPFP=0.0251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,300B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,328B, BPFP=0.0402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,316B, BPFP=0.0402 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,924B, BPFP=0.0736 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,860B, BPFP=0.0729 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,808B, BPFP=0.0627 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,488B, BPFP=0.0625 +⌛️ [2/4] FRONTEND: Frontend time: 31.903s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.428s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66985745 5.20522570 + layer.9.1 2.66884121 5.39599625 + layer.19.0 3.21935619 23.24163908 + layer.19.1 3.21606501 23.23486249 + layer.29.0 4.24164606 214.60147871 + layer.29.1 4.23648681 210.37268443 + layer.39.0 8.06392628 14459.72050699 + layer.39.1 8.17747540 14499.87650309 + ------------------------------------------------------------------------------------- + TOTAL 4.56170680 3680.20611209 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 633556 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 3680.206112 +---------------------- ---------------------------------------------------------- +Time: 64.352s Load: 1.020s, Pack+Encode: 31.903s, Decode+Unpack: 31.428s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3680.2061 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.168s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,080B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,652B, BPFP=0.0245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 55,564B, BPFP=0.0353 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 56,300B, BPFP=0.0357 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 107,316B, BPFP=0.0681 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 106,928B, BPFP=0.0679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,092B, BPFP=0.0705 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 108,024B, BPFP=0.0686 +⌛️ [2/4] FRONTEND: Frontend time: 32.493s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.437s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66795352 5.36108946 + layer.9.1 2.66862889 5.49429233 + layer.19.0 3.22250645 23.21420265 + layer.19.1 3.22577319 23.21052110 + layer.29.0 4.25792136 205.95041843 + layer.29.1 4.25014663 207.75154371 + layer.39.0 8.65209937 15850.89372766 + layer.39.1 8.58450170 15768.41598960 + ------------------------------------------------------------------------------------- + TOTAL 4.69119139 4011.28647312 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 622956 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 4011.286473 +---------------------- ---------------------------------------------------------- +Time: 65.099s Load: 1.168s, Pack+Encode: 32.493s, Decode+Unpack: 31.437s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4011.2865 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.182s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,092B, BPFP=0.0235 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,480B, BPFP=0.0238 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,088B, BPFP=0.0388 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,140B, BPFP=0.0382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,208B, BPFP=0.0731 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 117,408B, BPFP=0.0745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 109,412B, BPFP=0.0694 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 109,156B, BPFP=0.0693 +⌛️ [2/4] FRONTEND: Frontend time: 32.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092775 5.50674168 + layer.9.1 0.00093166 5.55226529 + layer.19.0 0.08227225 24.93102555 + layer.19.1 0.08381199 25.32662191 + layer.29.0 0.10725604 231.07625122 + layer.29.1 0.10756977 238.33579786 + layer.39.0 7.96294394 15040.80857979 + layer.39.1 7.95922050 15279.89080273 + ------------------------------------------------------------------------------------- + TOTAL 2.03811674 3856.42851075 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 646984 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 3856.428511 +---------------------- ---------------------------------------------------------- +Time: 64.358s Load: 1.182s, Pack+Encode: 32.160s, Decode+Unpack: 31.016s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3856.4285 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,840B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,196B, BPFP=0.0242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,680B, BPFP=0.0385 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,024B, BPFP=0.0387 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,108B, BPFP=0.0775 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,896B, BPFP=0.0774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,284B, BPFP=0.0827 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 128,924B, BPFP=0.0818 +⌛️ [2/4] FRONTEND: Frontend time: 32.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66308118 5.19124153 + layer.9.1 2.66351027 5.21400429 + layer.19.0 3.21594155 22.30164070 + layer.19.1 3.21498593 22.86864742 + layer.29.0 4.33566519 228.32643403 + layer.29.1 4.34101296 227.91261781 + layer.39.0 8.65310735 16683.24471888 + layer.39.1 8.66575030 16707.20051999 + ------------------------------------------------------------------------------------- + TOTAL 4.71913184 4237.78247808 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 700952 +BPFP 0.0556 bits/point +EBPFP 0.0556 equivalent bits/point +MSE 4237.782478 +---------------------- ---------------------------------------------------------- +Time: 65.203s Load: 1.078s, Pack+Encode: 32.651s, Decode+Unpack: 31.474s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4237.7825 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.031s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,396B, BPFP=0.0237 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,076B, BPFP=0.0242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,316B, BPFP=0.0440 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,532B, BPFP=0.0441 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 134,172B, BPFP=0.0852 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 134,840B, BPFP=0.0856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 150,944B, BPFP=0.0958 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 153,032B, BPFP=0.0971 +⌛️ [2/4] FRONTEND: Frontend time: 32.334s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.171s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.65959259 5.38409787 + layer.9.1 2.65993726 5.28352272 + layer.19.0 3.20866700 23.32712463 + layer.19.1 3.21007805 22.83279930 + layer.29.0 4.27255361 223.01787455 + layer.29.1 4.27602442 224.28347416 + layer.39.0 19.11658068 15212.71758206 + layer.39.1 9.60360322 14828.58108547 + ------------------------------------------------------------------------------------- + TOTAL 6.12587960 3818.17844510 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 787308 +BPFP 0.0625 bits/point +EBPFP 0.0625 equivalent bits/point +MSE 3818.178445 +---------------------- ---------------------------------------------------------- +Time: 64.536s Load: 1.031s, Pack+Encode: 32.334s, Decode+Unpack: 31.171s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3818.1784 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.233s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,848B, BPFP=0.0253 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,720B, BPFP=0.0252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,176B, BPFP=0.0388 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,072B, BPFP=0.0394 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,680B, BPFP=0.0722 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,576B, BPFP=0.0721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 127,020B, BPFP=0.0806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 127,200B, BPFP=0.0807 +⌛️ [2/4] FRONTEND: Frontend time: 32.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.225s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67078767 5.15140559 + layer.9.1 2.67131261 5.21205402 + layer.19.0 3.30595795 24.64602799 + layer.19.1 3.30543206 24.78353764 + layer.29.0 0.11228124 218.62298911 + layer.29.1 0.11507649 225.83271043 + layer.39.0 11.41791162 15377.53656159 + layer.39.1 11.38150745 15625.54176146 + ------------------------------------------------------------------------------------- + TOTAL 4.37253339 3938.41588098 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 684292 +BPFP 0.0543 bits/point +EBPFP 0.0543 equivalent bits/point +MSE 3938.415881 +---------------------- ---------------------------------------------------------- +Time: 65.000s Load: 1.233s, Pack+Encode: 32.541s, Decode+Unpack: 31.225s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3938.4159 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.136s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,696B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,924B, BPFP=0.0253 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,604B, BPFP=0.0429 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 65,976B, BPFP=0.0419 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 127,248B, BPFP=0.0808 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,104B, BPFP=0.0794 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 147,212B, BPFP=0.0934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 142,632B, BPFP=0.0905 +⌛️ [2/4] FRONTEND: Frontend time: 32.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14463971 5.26006174 + layer.9.1 0.14470460 5.33322775 + layer.19.0 0.12255537 24.77497106 + layer.19.1 0.11825690 25.27962758 + layer.29.0 0.11949990 225.76161846 + layer.29.1 0.11467140 229.79314267 + layer.39.0 10.68243977 17031.11602210 + layer.39.1 10.40156301 16975.76080598 + ------------------------------------------------------------------------------------- + TOTAL 2.73104133 4315.38493467 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 755396 +BPFP 0.0599 bits/point +EBPFP 0.0599 equivalent bits/point +MSE 4315.384935 +---------------------- ---------------------------------------------------------- +Time: 65.020s Load: 1.136s, Pack+Encode: 32.301s, Decode+Unpack: 31.583s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4315.3849 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.235s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,404B, BPFP=0.0256 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,720B, BPFP=0.0258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,272B, BPFP=0.0408 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,244B, BPFP=0.0401 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,872B, BPFP=0.0780 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,552B, BPFP=0.0772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 137,488B, BPFP=0.0873 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 137,864B, BPFP=0.0875 +⌛️ [2/4] FRONTEND: Frontend time: 32.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.305s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14485273 5.31117306 + layer.9.1 0.14484227 5.38015861 + layer.19.0 0.11969613 25.13943421 + layer.19.1 0.11916645 24.96018850 + layer.29.0 0.11480527 232.63397790 + layer.29.1 0.11451660 223.92742525 + layer.39.0 11.00270276 15534.50113747 + layer.39.1 11.01557422 15594.30874228 + ------------------------------------------------------------------------------------- + TOTAL 2.84701956 3955.77027966 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 728416 +BPFP 0.0578 bits/point +EBPFP 0.0578 equivalent bits/point +MSE 3955.770280 +---------------------- ---------------------------------------------------------- +Time: 65.180s Load: 1.235s, Pack+Encode: 32.641s, Decode+Unpack: 31.305s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3955.7703 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.236s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,752B, BPFP=0.0259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,960B, BPFP=0.0260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,132B, BPFP=0.0375 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,064B, BPFP=0.0369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 105,316B, BPFP=0.0668 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,912B, BPFP=0.0660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 125,948B, BPFP=0.0799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 123,324B, BPFP=0.0783 +⌛️ [2/4] FRONTEND: Frontend time: 32.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.164s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14481488 5.27362475 + layer.9.1 0.14470567 5.31811722 + layer.19.0 0.03819180 23.71463936 + layer.19.1 0.04002141 24.34841668 + layer.29.0 0.11241068 214.63422164 + layer.29.1 0.11133552 226.83240575 + layer.39.0 31.78807483 14770.84302892 + layer.39.1 43.50691623 15657.04777381 + ------------------------------------------------------------------------------------- + TOTAL 9.48580888 3866.00152852 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 657408 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 3866.001529 +---------------------- ---------------------------------------------------------- +Time: 65.045s Load: 1.236s, Pack+Encode: 32.646s, Decode+Unpack: 31.164s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3866.0015 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.166s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,416B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,412B, BPFP=0.0257 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,696B, BPFP=0.0411 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,236B, BPFP=0.0401 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 131,260B, BPFP=0.0833 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 130,848B, BPFP=0.0831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,788B, BPFP=0.1103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 176,736B, BPFP=0.1122 +⌛️ [2/4] FRONTEND: Frontend time: 32.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.813s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14528272 5.27530303 + layer.9.1 0.14516892 5.22886759 + layer.19.0 0.11319376 24.48054873 + layer.19.1 0.11666145 24.24847152 + layer.29.0 0.21118872 240.16397871 + layer.29.1 0.20646930 238.15266493 + layer.39.0 14.37750853 17457.95775106 + layer.39.1 21.76644002 17214.67403315 + ------------------------------------------------------------------------------------- + TOTAL 4.63523918 4401.27270234 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 821392 +BPFP 0.0652 bits/point +EBPFP 0.0652 equivalent bits/point +MSE 4401.272702 +---------------------- ---------------------------------------------------------- +Time: 64.491s Load: 1.166s, Pack+Encode: 32.511s, Decode+Unpack: 30.813s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4401.2727 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.104s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,096B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,132B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,360B, BPFP=0.0402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,208B, BPFP=0.0401 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,280B, BPFP=0.0732 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,404B, BPFP=0.0733 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 129,884B, BPFP=0.0824 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 126,676B, BPFP=0.0804 +⌛️ [2/4] FRONTEND: Frontend time: 32.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.876s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14454765 5.48475966 + layer.9.1 0.14475082 5.42068548 + layer.19.0 0.04087094 25.22128646 + layer.19.1 0.11687931 25.21740687 + layer.29.0 0.10817139 212.23090673 + layer.29.1 0.10802081 216.77433377 + layer.39.0 19.80422286 15759.95970101 + layer.39.1 34.29222355 15806.52583685 + ------------------------------------------------------------------------------------- + TOTAL 6.84496092 4007.10436460 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 692040 +BPFP 0.0549 bits/point +EBPFP 0.0549 equivalent bits/point +MSE 4007.104365 +---------------------- ---------------------------------------------------------- +Time: 64.270s Load: 1.104s, Pack+Encode: 32.290s, Decode+Unpack: 30.876s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4007.1044 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.022s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,852B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,084B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,916B, BPFP=0.0399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,824B, BPFP=0.0399 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,164B, BPFP=0.0712 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,664B, BPFP=0.0721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 115,588B, BPFP=0.0734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 113,656B, BPFP=0.0721 +⌛️ [2/4] FRONTEND: Frontend time: 32.016s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.236s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14462205 5.36277980 + layer.9.1 0.14495783 5.48383292 + layer.19.0 0.04322015 24.91205415 + layer.19.1 0.03788725 24.98271449 + layer.29.0 0.10021623 195.80344085 + layer.29.1 0.10137775 203.00595141 + layer.39.0 58.66958482 15520.88787780 + layer.39.1 72.48303949 15637.59896003 + ------------------------------------------------------------------------------------- + TOTAL 16.46561320 3952.25470143 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 658748 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 3952.254701 +---------------------- ---------------------------------------------------------- +Time: 64.274s Load: 1.022s, Pack+Encode: 32.016s, Decode+Unpack: 31.236s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3952.2547 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.241s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,280B, BPFP=0.0256 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,812B, BPFP=0.0259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 71,424B, BPFP=0.0453 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,240B, BPFP=0.0446 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 133,444B, BPFP=0.0847 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 133,564B, BPFP=0.0848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,920B, BPFP=0.1028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 159,668B, BPFP=0.1013 +⌛️ [2/4] FRONTEND: Frontend time: 32.060s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.108s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527927 5.19061821 + layer.9.1 0.14528875 5.24083136 + layer.19.0 0.12591341 22.73888934 + layer.19.1 0.13556211 23.57182574 + layer.29.0 0.11238900 214.48578161 + layer.29.1 0.11028371 219.56800455 + layer.39.0 11.48751193 16550.52713682 + layer.39.1 11.29491489 16842.20604485 + ------------------------------------------------------------------------------------- + TOTAL 2.94464289 4235.44114156 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 811352 +BPFP 0.0644 bits/point +EBPFP 0.0644 equivalent bits/point +MSE 4235.441142 +---------------------- ---------------------------------------------------------- +Time: 64.408s Load: 1.241s, Pack+Encode: 32.060s, Decode+Unpack: 31.108s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4235.4411 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.231s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,912B, BPFP=0.0253 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,064B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,276B, BPFP=0.0414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,180B, BPFP=0.0420 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 120,248B, BPFP=0.0763 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,184B, BPFP=0.0769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 134,456B, BPFP=0.0853 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 132,744B, BPFP=0.0843 +⌛️ [2/4] FRONTEND: Frontend time: 32.866s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14489184 5.30952366 + layer.9.1 0.14511764 5.26092817 + layer.19.0 0.03976490 25.19409124 + layer.19.1 0.11370806 24.97679101 + layer.29.0 0.10933599 221.53834904 + layer.29.1 0.11012027 232.18353916 + layer.39.0 9.10787636 15611.56841079 + layer.39.1 9.00026152 15293.69125772 + ------------------------------------------------------------------------------------- + TOTAL 2.34638457 3927.46536135 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 720064 +BPFP 0.0571 bits/point +EBPFP 0.0571 equivalent bits/point +MSE 3927.465361 +---------------------- ---------------------------------------------------------- +Time: 65.557s Load: 1.231s, Pack+Encode: 32.866s, Decode+Unpack: 31.461s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3927.4654 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.155s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,196B, BPFP=0.0268 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,540B, BPFP=0.0270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,016B, BPFP=0.0457 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,788B, BPFP=0.0443 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 120,728B, BPFP=0.0766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,280B, BPFP=0.0770 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 120,148B, BPFP=0.0763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 120,736B, BPFP=0.0766 +⌛️ [2/4] FRONTEND: Frontend time: 32.447s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.197s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083307 5.00138185 + layer.9.1 0.00247171 5.08109499 + layer.19.0 0.00642632 22.67473899 + layer.19.1 0.00641681 23.12766087 + layer.29.0 0.10256791 222.32056386 + layer.29.1 0.10162673 230.57212788 + layer.39.0 8.50517638 15893.99025024 + layer.39.1 8.55767781 15986.37764056 + ------------------------------------------------------------------------------------- + TOTAL 2.16039959 4048.64318241 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 709432 +BPFP 0.0563 bits/point +EBPFP 0.0563 equivalent bits/point +MSE 4048.643182 +---------------------- ---------------------------------------------------------- +Time: 64.798s Load: 1.155s, Pack+Encode: 32.447s, Decode+Unpack: 31.197s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4048.6432 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.119s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,196B, BPFP=0.0249 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,212B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,576B, BPFP=0.0397 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,072B, BPFP=0.0400 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 125,212B, BPFP=0.0795 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 126,112B, BPFP=0.0800 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 146,500B, BPFP=0.0930 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 144,940B, BPFP=0.0920 +⌛️ [2/4] FRONTEND: Frontend time: 32.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.259s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00064391 5.32281754 + layer.9.1 0.00065402 5.04274976 + layer.19.0 0.08134466 24.28633815 + layer.19.1 0.08141702 23.78544950 + layer.29.0 0.11551180 236.23700032 + layer.29.1 0.11251285 228.45366835 + layer.39.0 10.61319619 16784.18979526 + layer.39.1 10.43102047 16329.31426714 + ------------------------------------------------------------------------------------- + TOTAL 2.67953762 4204.57901075 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 746820 +BPFP 0.0593 bits/point +EBPFP 0.0593 equivalent bits/point +MSE 4204.579011 +---------------------- ---------------------------------------------------------- +Time: 65.015s Load: 1.119s, Pack+Encode: 32.637s, Decode+Unpack: 31.259s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4204.5790 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,720B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,736B, BPFP=0.0246 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,444B, BPFP=0.0371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,484B, BPFP=0.0365 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 118,132B, BPFP=0.0750 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 119,056B, BPFP=0.0756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 157,360B, BPFP=0.0999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 158,492B, BPFP=0.1006 +⌛️ [2/4] FRONTEND: Frontend time: 32.455s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14441776 5.19776993 + layer.9.1 0.14449203 5.35639866 + layer.19.0 0.11315974 24.52905630 + layer.19.1 0.11435745 24.81392692 + layer.29.0 0.12811458 223.30252681 + layer.29.1 0.12952277 226.47607247 + layer.39.0 31.10682331 16721.51316217 + layer.39.1 16.99297713 15887.84010400 + ------------------------------------------------------------------------------------- + TOTAL 6.10923309 4139.87862716 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 746424 +BPFP 0.0592 bits/point +EBPFP 0.0592 equivalent bits/point +MSE 4139.878627 +---------------------- ---------------------------------------------------------- +Time: 64.659s Load: 1.178s, Pack+Encode: 32.455s, Decode+Unpack: 31.027s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4139.8786 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,560B, BPFP=0.0238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,100B, BPFP=0.0242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,304B, BPFP=0.0389 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,484B, BPFP=0.0390 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,724B, BPFP=0.0735 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 116,208B, BPFP=0.0738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 148,064B, BPFP=0.0940 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 150,888B, BPFP=0.0958 +⌛️ [2/4] FRONTEND: Frontend time: 32.435s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078516 5.24476236 + layer.9.1 0.00079184 5.41506858 + layer.19.0 3.22632161 24.54041325 + layer.19.1 3.22513146 24.32350910 + layer.29.0 0.10494786 231.43595629 + layer.29.1 0.10251782 230.99784693 + layer.39.0 10.88842496 16329.64835879 + layer.39.1 10.78217420 16131.43451414 + ------------------------------------------------------------------------------------- + TOTAL 3.54138686 4122.88005368 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 729332 +BPFP 0.0579 bits/point +EBPFP 0.0579 equivalent bits/point +MSE 4122.880054 +---------------------- ---------------------------------------------------------- +Time: 64.798s Load: 1.072s, Pack+Encode: 32.435s, Decode+Unpack: 31.290s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4122.8801 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.094s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,016B, BPFP=0.0260 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,988B, BPFP=0.0260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,308B, BPFP=0.0440 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,012B, BPFP=0.0444 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 140,492B, BPFP=0.0892 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 140,948B, BPFP=0.0895 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 156,108B, BPFP=0.0991 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 159,080B, BPFP=0.1010 +⌛️ [2/4] FRONTEND: Frontend time: 31.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.394s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561636 5.27094420 + layer.9.1 0.14552785 5.33260570 + layer.19.0 0.04069186 24.02062175 + layer.19.1 0.03840616 23.71996110 + layer.29.0 0.11346353 230.87567030 + layer.29.1 0.11182956 216.95766981 + layer.39.0 10.19697364 17015.39161521 + layer.39.1 10.11578978 16993.08937277 + ------------------------------------------------------------------------------------- + TOTAL 2.61353734 4314.33230760 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 817952 +BPFP 0.0649 bits/point +EBPFP 0.0649 equivalent bits/point +MSE 4314.332308 +---------------------- ---------------------------------------------------------- +Time: 64.063s Load: 1.094s, Pack+Encode: 31.575s, Decode+Unpack: 31.394s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4314.3323 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.211s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,544B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,416B, BPFP=0.0244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,008B, BPFP=0.0400 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,016B, BPFP=0.0406 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 121,756B, BPFP=0.0773 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,740B, BPFP=0.0779 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 143,744B, BPFP=0.0912 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 142,608B, BPFP=0.0905 +⌛️ [2/4] FRONTEND: Frontend time: 32.538s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.776s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14399445 5.43512794 + layer.9.1 0.14558028 5.37546527 + layer.19.0 0.03837104 24.17324352 + layer.19.1 0.04376782 24.25737833 + layer.29.0 0.11695251 231.53792249 + layer.29.1 0.13128335 232.62309067 + layer.39.0 11.28613757 16175.71010725 + layer.39.1 11.84408769 16260.94767631 + ------------------------------------------------------------------------------------- + TOTAL 2.96877184 4120.00750147 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 734832 +BPFP 0.0583 bits/point +EBPFP 0.0583 equivalent bits/point +MSE 4120.007501 +---------------------- ---------------------------------------------------------- +Time: 64.525s Load: 1.211s, Pack+Encode: 32.538s, Decode+Unpack: 30.776s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4120.0075 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.151s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,276B, BPFP=0.0243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,988B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,500B, BPFP=0.0403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,612B, BPFP=0.0410 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 130,632B, BPFP=0.0829 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 134,508B, BPFP=0.0854 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 178,144B, BPFP=0.1131 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 183,720B, BPFP=0.1166 +⌛️ [2/4] FRONTEND: Frontend time: 32.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14502686 5.49708523 + layer.9.1 0.03259508 5.43188373 + layer.19.0 0.11326540 25.34352149 + layer.19.1 0.11324834 23.81362985 + layer.29.0 0.12250664 246.79854566 + layer.29.1 0.12058897 234.73779249 + layer.39.0 16.17915050 16578.97432564 + layer.39.1 21.66230805 17418.40233994 + ------------------------------------------------------------------------------------- + TOTAL 4.81108623 4317.37489051 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 831380 +BPFP 0.0660 bits/point +EBPFP 0.0660 equivalent bits/point +MSE 4317.374891 +---------------------- ---------------------------------------------------------- +Time: 64.760s Load: 1.151s, Pack+Encode: 32.611s, Decode+Unpack: 30.998s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4317.3749 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.147s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,476B, BPFP=0.0232 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,060B, BPFP=0.0235 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 52,728B, BPFP=0.0335 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 52,040B, BPFP=0.0330 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,648B, BPFP=0.0613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 95,544B, BPFP=0.0606 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,552B, BPFP=0.0594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,128B, BPFP=0.0597 +⌛️ [2/4] FRONTEND: Frontend time: 32.457s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.328s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66733019 5.55057876 + layer.9.1 2.66763138 5.35091442 + layer.19.0 3.22293078 22.95828679 + layer.19.1 3.22376992 22.86074860 + layer.29.0 4.27658332 190.65347741 + layer.29.1 4.27160529 195.62168915 + layer.39.0 7.81683598 14956.89047774 + layer.39.1 9.86231960 14945.10757231 + ------------------------------------------------------------------------------------- + TOTAL 4.75112581 3793.12421815 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 558176 +BPFP 0.0443 bits/point +EBPFP 0.0443 equivalent bits/point +MSE 3793.124218 +---------------------- ---------------------------------------------------------- +Time: 64.933s Load: 1.147s, Pack+Encode: 32.457s, Decode+Unpack: 31.328s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3793.1242 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.039s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,028B, BPFP=0.0260 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,856B, BPFP=0.0253 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 59,712B, BPFP=0.0379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,792B, BPFP=0.0386 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 108,720B, BPFP=0.0690 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 108,948B, BPFP=0.0692 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,032B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,316B, BPFP=0.0618 +⌛️ [2/4] FRONTEND: Frontend time: 32.116s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.148s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549763 5.32545461 + layer.9.1 0.14520254 5.26554439 + layer.19.0 0.04746155 24.06303573 + layer.19.1 0.04383140 24.12066085 + layer.29.0 4.26247378 202.16275999 + layer.29.1 4.25497898 210.21506337 + layer.39.0 7.94138086 14266.64673383 + layer.39.1 7.86439079 14499.10042249 + ------------------------------------------------------------------------------------- + TOTAL 3.08815219 3654.61245941 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 613404 +BPFP 0.0487 bits/point +EBPFP 0.0487 equivalent bits/point +MSE 3654.612459 +---------------------- ---------------------------------------------------------- +Time: 64.303s Load: 1.039s, Pack+Encode: 32.116s, Decode+Unpack: 31.148s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3654.6125 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.022s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,052B, BPFP=0.0254 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,708B, BPFP=0.0258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,768B, BPFP=0.0411 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,736B, BPFP=0.0411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,744B, BPFP=0.0779 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,104B, BPFP=0.0769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,088B, BPFP=0.0896 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 138,296B, BPFP=0.0878 +⌛️ [2/4] FRONTEND: Frontend time: 32.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.887s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11283708 5.17631349 + layer.9.1 0.11300174 5.24428947 + layer.19.0 3.22718329 23.38904066 + layer.19.1 3.22892155 22.15076576 + layer.29.0 4.26448309 210.30608141 + layer.29.1 4.25758082 214.99197676 + layer.39.0 9.82393946 15294.45953851 + layer.39.1 9.78394007 14821.80565486 + ------------------------------------------------------------------------------------- + TOTAL 4.35148589 3824.69045762 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 733496 +BPFP 0.0582 bits/point +EBPFP 0.0582 equivalent bits/point +MSE 3824.690458 +---------------------- ---------------------------------------------------------- +Time: 64.430s Load: 1.022s, Pack+Encode: 32.521s, Decode+Unpack: 30.887s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3824.6905 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.039s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,264B, BPFP=0.0262 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,168B, BPFP=0.0261 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,636B, BPFP=0.0404 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,552B, BPFP=0.0410 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 109,088B, BPFP=0.0692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 109,432B, BPFP=0.0695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 132,072B, BPFP=0.0838 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 130,904B, BPFP=0.0831 +⌛️ [2/4] FRONTEND: Frontend time: 32.425s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14503861 5.26220941 + layer.9.1 0.14483112 5.16289869 + layer.19.0 0.11529889 22.30224752 + layer.19.1 0.11517203 22.67865413 + layer.29.0 0.11961639 224.37329379 + layer.29.1 0.11795276 216.86305655 + layer.39.0 83.84633978 15823.71400715 + layer.39.1 174.87768118 15851.10042249 + ------------------------------------------------------------------------------------- + TOTAL 32.43524135 4021.43209872 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 692116 +BPFP 0.0549 bits/point +EBPFP 0.0549 equivalent bits/point +MSE 4021.432099 +---------------------- ---------------------------------------------------------- +Time: 64.446s Load: 1.039s, Pack+Encode: 32.425s, Decode+Unpack: 30.982s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4021.4321 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.242s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,856B, BPFP=0.0253 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,980B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,856B, BPFP=0.0393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,336B, BPFP=0.0396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 128,544B, BPFP=0.0816 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 126,836B, BPFP=0.0805 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,560B, BPFP=0.0772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 121,744B, BPFP=0.0773 +⌛️ [2/4] FRONTEND: Frontend time: 32.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.255s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.81453913 5.31535987 + layer.9.1 0.14528001 5.32868231 + layer.19.0 3.26598681 23.43473249 + layer.19.1 0.04116655 23.93641585 + layer.29.0 4.28557138 228.89911033 + layer.29.1 4.28198282 219.91221157 + layer.39.0 74.89367180 16048.68898278 + layer.39.1 42.04871577 15741.29476763 + ------------------------------------------------------------------------------------- + TOTAL 16.47211428 4037.10128285 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 702712 +BPFP 0.0558 bits/point +EBPFP 0.0558 equivalent bits/point +MSE 4037.101283 +---------------------- ---------------------------------------------------------- +Time: 65.094s Load: 1.242s, Pack+Encode: 32.597s, Decode+Unpack: 31.255s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4037.1013 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,900B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,816B, BPFP=0.0246 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,168B, BPFP=0.0407 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,228B, BPFP=0.0401 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 126,660B, BPFP=0.0804 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 123,184B, BPFP=0.0782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,144B, BPFP=0.0769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 120,184B, BPFP=0.0763 +⌛️ [2/4] FRONTEND: Frontend time: 32.373s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.930s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66710422 5.43036668 + layer.9.1 2.66812426 5.22276543 + layer.19.0 3.22059776 23.27794422 + layer.19.1 3.22546153 23.93167808 + layer.29.0 0.11226317 222.15292899 + layer.29.1 0.11257672 220.50375772 + layer.39.0 59.39237691 15465.37146571 + layer.39.1 37.52358222 15153.62105947 + ------------------------------------------------------------------------------------- + TOTAL 13.61526085 3889.93899579 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 696284 +BPFP 0.0552 bits/point +EBPFP 0.0552 equivalent bits/point +MSE 3889.938996 +---------------------- ---------------------------------------------------------- +Time: 64.476s Load: 1.174s, Pack+Encode: 32.373s, Decode+Unpack: 30.930s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3889.9390 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.190s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,744B, BPFP=0.0259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,208B, BPFP=0.0262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,140B, BPFP=0.0369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 56,984B, BPFP=0.0362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,572B, BPFP=0.0645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,740B, BPFP=0.0652 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,632B, BPFP=0.0709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 113,900B, BPFP=0.0723 +⌛️ [2/4] FRONTEND: Frontend time: 32.353s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.124s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519123 5.35761992 + layer.9.1 0.14511500 5.11190829 + layer.19.0 0.03974548 22.55886923 + layer.19.1 0.03981401 22.75549439 + layer.29.0 4.26343511 203.58752437 + layer.29.1 4.25610090 203.75546393 + layer.39.0 7.90972018 15375.46701332 + layer.39.1 8.05601540 16142.33474163 + ------------------------------------------------------------------------------------- + TOTAL 3.10689216 3997.61607939 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 626920 +BPFP 0.0497 bits/point +EBPFP 0.0497 equivalent bits/point +MSE 3997.616079 +---------------------- ---------------------------------------------------------- +Time: 64.668s Load: 1.190s, Pack+Encode: 32.353s, Decode+Unpack: 31.124s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3997.6161 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.147s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,360B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,848B, BPFP=0.0253 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,104B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,008B, BPFP=0.0362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 107,820B, BPFP=0.0684 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 109,972B, BPFP=0.0698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 126,264B, BPFP=0.0801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 122,516B, BPFP=0.0778 +⌛️ [2/4] FRONTEND: Frontend time: 31.711s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.248s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14586543 5.17341522 + layer.9.1 0.14572574 5.20962515 + layer.19.0 0.03953905 24.12412405 + layer.19.1 0.03760033 23.57089139 + layer.29.0 0.10448607 219.30149090 + layer.29.1 0.10697372 216.37083604 + layer.39.0 14.19073468 16221.27266818 + layer.39.1 8.92149669 16995.56451089 + ------------------------------------------------------------------------------------- + TOTAL 2.96155271 4213.82344523 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 659892 +BPFP 0.0524 bits/point +EBPFP 0.0524 equivalent bits/point +MSE 4213.823445 +---------------------- ---------------------------------------------------------- +Time: 64.106s Load: 1.147s, Pack+Encode: 31.711s, Decode+Unpack: 31.248s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4213.8234 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.043s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,980B, BPFP=0.0241 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,544B, BPFP=0.0245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,560B, BPFP=0.0403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,928B, BPFP=0.0399 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 118,052B, BPFP=0.0749 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 118,808B, BPFP=0.0754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 126,196B, BPFP=0.0801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 122,684B, BPFP=0.0779 +⌛️ [2/4] FRONTEND: Frontend time: 31.777s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00251883 5.27015584 + layer.9.1 0.14409062 5.05151153 + layer.19.0 0.12740102 24.37547733 + layer.19.1 0.12254588 24.51971279 + layer.29.0 4.25147928 224.34304924 + layer.29.1 4.25065697 218.55758450 + layer.39.0 9.21805114 15311.69970751 + layer.39.1 9.03214690 15175.81800455 + ------------------------------------------------------------------------------------- + TOTAL 3.39361133 3873.70440041 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 688752 +BPFP 0.0546 bits/point +EBPFP 0.0546 equivalent bits/point +MSE 3873.704400 +---------------------- ---------------------------------------------------------- +Time: 64.223s Load: 1.043s, Pack+Encode: 31.777s, Decode+Unpack: 31.404s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3873.7044 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.982s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,448B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,372B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,844B, BPFP=0.0367 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,480B, BPFP=0.0378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,172B, BPFP=0.0718 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,804B, BPFP=0.0729 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 125,408B, BPFP=0.0796 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 125,664B, BPFP=0.0798 +⌛️ [2/4] FRONTEND: Frontend time: 32.171s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.351s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550905 5.48544709 + layer.9.1 0.14590163 5.37871646 + layer.19.0 0.12839093 24.60198143 + layer.19.1 0.12422524 24.09922916 + layer.29.0 0.11695262 218.35617078 + layer.29.1 0.11389293 222.32145759 + layer.39.0 10.18180439 16203.40591485 + layer.39.1 10.42432323 16222.78323042 + ------------------------------------------------------------------------------------- + TOTAL 2.67262500 4115.80401847 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 675192 +BPFP 0.0536 bits/point +EBPFP 0.0536 equivalent bits/point +MSE 4115.804018 +---------------------- ---------------------------------------------------------- +Time: 64.503s Load: 0.982s, Pack+Encode: 32.171s, Decode+Unpack: 31.351s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4115.8040 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.067s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,040B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,924B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,136B, BPFP=0.0382 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,072B, BPFP=0.0388 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 119,992B, BPFP=0.0762 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 120,232B, BPFP=0.0763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,592B, BPFP=0.0899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 142,928B, BPFP=0.0907 +⌛️ [2/4] FRONTEND: Frontend time: 32.269s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.135s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509775 5.34738204 + layer.9.1 0.14508723 5.19390018 + layer.19.0 0.11633494 24.15571427 + layer.19.1 0.11804005 24.55168386 + layer.29.0 0.15409572 226.08214170 + layer.29.1 0.14997486 224.41928014 + layer.39.0 9.23291952 16893.74455639 + layer.39.1 9.22304726 17248.46408840 + ------------------------------------------------------------------------------------- + TOTAL 2.41057467 4331.49484337 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 723916 +BPFP 0.0574 bits/point +EBPFP 0.0574 equivalent bits/point +MSE 4331.494843 +---------------------- ---------------------------------------------------------- +Time: 64.472s Load: 1.067s, Pack+Encode: 32.269s, Decode+Unpack: 31.135s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4331.4948 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.041s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,532B, BPFP=0.0251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,472B, BPFP=0.0251 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,856B, BPFP=0.0393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,064B, BPFP=0.0394 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 121,148B, BPFP=0.0769 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 120,672B, BPFP=0.0766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 144,856B, BPFP=0.0919 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 147,624B, BPFP=0.0937 +⌛️ [2/4] FRONTEND: Frontend time: 32.978s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497060 5.17915304 + layer.9.1 0.14492971 5.24787041 + layer.19.0 0.11929473 23.24965216 + layer.19.1 0.11869117 24.03468019 + layer.29.0 0.13715227 234.82210757 + layer.29.1 0.14278979 238.70043874 + layer.39.0 9.99110525 17824.98537537 + layer.39.1 10.01170034 18038.11894703 + ------------------------------------------------------------------------------------- + TOTAL 2.60132923 4549.29227806 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 737224 +BPFP 0.0585 bits/point +EBPFP 0.0585 equivalent bits/point +MSE 4549.292278 +---------------------- ---------------------------------------------------------- +Time: 65.546s Load: 1.041s, Pack+Encode: 32.978s, Decode+Unpack: 31.527s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4549.2923 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.084s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,472B, BPFP=0.0238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,748B, BPFP=0.0240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,896B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,740B, BPFP=0.0379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 116,820B, BPFP=0.0742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 116,356B, BPFP=0.0739 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 137,492B, BPFP=0.0873 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 132,720B, BPFP=0.0842 +⌛️ [2/4] FRONTEND: Frontend time: 32.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.181s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14589856 5.29701591 + layer.9.1 0.03321603 5.28144804 + layer.19.0 0.11866178 24.08771734 + layer.19.1 0.11267978 23.91392032 + layer.29.0 0.10803594 227.11065973 + layer.29.1 0.10714094 227.41367403 + layer.39.0 11.58943751 16883.59961001 + layer.39.1 9.70079103 16825.22716932 + ------------------------------------------------------------------------------------- + TOTAL 2.73948269 4277.74140184 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 699244 +BPFP 0.0555 bits/point +EBPFP 0.0555 equivalent bits/point +MSE 4277.741402 +---------------------- ---------------------------------------------------------- +Time: 64.854s Load: 1.084s, Pack+Encode: 32.588s, Decode+Unpack: 31.181s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4277.7414 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.147s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,412B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,312B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,804B, BPFP=0.0399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,856B, BPFP=0.0405 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 129,036B, BPFP=0.0819 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 129,680B, BPFP=0.0823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,148B, BPFP=0.0985 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 156,216B, BPFP=0.0992 +⌛️ [2/4] FRONTEND: Frontend time: 32.390s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.316s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03308301 5.33910490 + layer.9.1 0.14566304 5.29845235 + layer.19.0 0.03810260 24.24440912 + layer.19.1 0.03780774 25.12421545 + layer.29.0 0.11592613 232.92764868 + layer.29.1 0.11717217 235.48425821 + layer.39.0 9.98032847 16594.46473838 + layer.39.1 9.70849498 16494.96392590 + ------------------------------------------------------------------------------------- + TOTAL 2.52207227 4202.23084413 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 775464 +BPFP 0.0615 bits/point +EBPFP 0.0615 equivalent bits/point +MSE 4202.230844 +---------------------- ---------------------------------------------------------- +Time: 64.853s Load: 1.147s, Pack+Encode: 32.390s, Decode+Unpack: 31.316s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4202.2308 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.129s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,396B, BPFP=0.0263 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,252B, BPFP=0.0262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,760B, BPFP=0.0462 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,608B, BPFP=0.0455 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 137,308B, BPFP=0.0872 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 141,768B, BPFP=0.0900 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 152,208B, BPFP=0.0966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 152,496B, BPFP=0.0968 +⌛️ [2/4] FRONTEND: Frontend time: 32.358s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14555504 5.22990985 + layer.9.1 0.14557384 5.20138674 + layer.19.0 0.03995539 23.54616916 + layer.19.1 0.04542811 24.50873162 + layer.29.0 0.12033866 214.95251056 + layer.29.1 0.13252172 224.88036237 + layer.39.0 10.37566776 16401.68345791 + layer.39.1 9.84188447 16091.15242119 + ------------------------------------------------------------------------------------- + TOTAL 2.60586562 4123.89436867 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 810796 +BPFP 0.0643 bits/point +EBPFP 0.0643 equivalent bits/point +MSE 4123.894369 +---------------------- ---------------------------------------------------------- +Time: 64.563s Load: 1.129s, Pack+Encode: 32.358s, Decode+Unpack: 31.076s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4123.8944 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.185s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,272B, BPFP=0.0243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,308B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,524B, BPFP=0.0365 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,136B, BPFP=0.0363 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 109,544B, BPFP=0.0695 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 109,868B, BPFP=0.0697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 132,624B, BPFP=0.0842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 136,016B, BPFP=0.0863 +⌛️ [2/4] FRONTEND: Frontend time: 33.036s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.134s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14470203 5.43376958 + layer.9.1 0.14481130 5.31220738 + layer.19.0 0.11257574 24.73156941 + layer.19.1 0.11422884 25.33689470 + layer.29.0 0.10456927 212.24721726 + layer.29.1 0.10551051 213.06176877 + layer.39.0 10.36536069 16697.28306792 + layer.39.1 11.81531702 16859.69840754 + ------------------------------------------------------------------------------------- + TOTAL 2.86338443 4255.38811282 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 679292 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 4255.388113 +---------------------- ---------------------------------------------------------- +Time: 65.355s Load: 1.185s, Pack+Encode: 33.036s, Decode+Unpack: 31.134s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4255.3881 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.085s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,032B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,024B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 68,496B, BPFP=0.0435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,840B, BPFP=0.0431 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 121,576B, BPFP=0.0772 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 121,740B, BPFP=0.0773 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 143,512B, BPFP=0.0911 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 144,068B, BPFP=0.0914 +⌛️ [2/4] FRONTEND: Frontend time: 32.500s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.206s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14530003 5.32131255 + layer.9.1 0.14546206 5.37194178 + layer.19.0 0.11891763 22.06451850 + layer.19.1 0.11677460 22.96570828 + layer.29.0 4.29725807 222.17779087 + layer.29.1 4.29692800 218.29111147 + layer.39.0 11.61914761 16812.85147871 + layer.39.1 11.22064282 16690.45953851 + ------------------------------------------------------------------------------------- + TOTAL 3.99505385 4249.93792508 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 745288 +BPFP 0.0591 bits/point +EBPFP 0.0591 equivalent bits/point +MSE 4249.937925 +---------------------- ---------------------------------------------------------- +Time: 64.791s Load: 1.085s, Pack+Encode: 32.500s, Decode+Unpack: 31.206s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4249.9379 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.969s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,348B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,836B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,324B, BPFP=0.0370 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,252B, BPFP=0.0370 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,036B, BPFP=0.0711 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 110,196B, BPFP=0.0699 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 123,612B, BPFP=0.0785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 124,796B, BPFP=0.0792 +⌛️ [2/4] FRONTEND: Frontend time: 32.978s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.235s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67106792 5.20319451 + layer.9.1 2.67195307 5.28231638 + layer.19.0 0.08237472 24.39188688 + layer.19.1 0.08192194 24.72130677 + layer.29.0 0.11152953 211.17362691 + layer.29.1 0.11703055 218.58193858 + layer.39.0 163.01811830 15964.33409165 + layer.39.1 58.15221299 15973.16217095 + ------------------------------------------------------------------------------------- + TOTAL 28.36327613 4053.35631658 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 665400 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 4053.356317 +---------------------- ---------------------------------------------------------- +Time: 65.181s Load: 0.969s, Pack+Encode: 32.978s, Decode+Unpack: 31.235s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4053.3563 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.918s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,212B, BPFP=0.0249 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,692B, BPFP=0.0252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,016B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,312B, BPFP=0.0389 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,624B, BPFP=0.0721 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,676B, BPFP=0.0728 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 123,580B, BPFP=0.0784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 126,220B, BPFP=0.0801 +⌛️ [2/4] FRONTEND: Frontend time: 32.417s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.086s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14620710 5.28008841 + layer.9.1 0.14642976 5.05469988 + layer.19.0 0.11726453 24.42761314 + layer.19.1 0.11958517 24.50576099 + layer.29.0 0.10693079 232.95588235 + layer.29.1 0.10826971 230.28101641 + layer.39.0 43.01306569 17303.56191095 + layer.39.1 17.12450997 17441.08417290 + ------------------------------------------------------------------------------------- + TOTAL 7.61028284 4408.39389313 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 679332 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 4408.393893 +---------------------- ---------------------------------------------------------- +Time: 64.421s Load: 0.918s, Pack+Encode: 32.417s, Decode+Unpack: 31.086s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4408.3939 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.860s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,652B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,008B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,960B, BPFP=0.0406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,860B, BPFP=0.0412 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 118,148B, BPFP=0.0750 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 116,976B, BPFP=0.0743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 113,092B, BPFP=0.0718 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 114,592B, BPFP=0.0727 +⌛️ [2/4] FRONTEND: Frontend time: 32.794s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.120s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03173930 5.31063415 + layer.9.1 0.03345565 5.45382449 + layer.19.0 3.26068347 24.40558732 + layer.19.1 3.26087326 23.55704877 + layer.29.0 4.24610771 207.92401284 + layer.29.1 4.24089229 200.73793468 + layer.39.0 8.81319124 15948.62918427 + layer.39.1 8.71779153 15876.36529087 + ------------------------------------------------------------------------------------- + TOTAL 4.07559181 4036.54793967 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 670288 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 4036.547940 +---------------------- ---------------------------------------------------------- +Time: 64.774s Load: 0.860s, Pack+Encode: 32.794s, Decode+Unpack: 31.120s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4036.5479 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.807s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,940B, BPFP=0.0234 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,172B, BPFP=0.0236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 53,048B, BPFP=0.0337 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 52,316B, BPFP=0.0332 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 106,616B, BPFP=0.0677 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 107,864B, BPFP=0.0685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 100,512B, BPFP=0.0638 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 103,000B, BPFP=0.0654 +⌛️ [2/4] FRONTEND: Frontend time: 32.352s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.083s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078599 5.51045877 + layer.9.1 0.00079117 5.15248562 + layer.19.0 0.00795310 24.27289923 + layer.19.1 0.00811505 23.83818959 + layer.29.0 4.25797468 211.44867160 + layer.29.1 4.25504309 213.84812723 + layer.39.0 81.06806549 15517.45076373 + layer.39.1 44.82015254 15597.10497238 + ------------------------------------------------------------------------------------- + TOTAL 16.80236014 3949.82832102 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 597468 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 3949.828321 +---------------------- ---------------------------------------------------------- +Time: 64.243s Load: 0.807s, Pack+Encode: 32.352s, Decode+Unpack: 31.083s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3949.8283 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.803s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,852B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,592B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,980B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,324B, BPFP=0.0389 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 119,604B, BPFP=0.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 120,616B, BPFP=0.0766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 117,272B, BPFP=0.0744 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 122,320B, BPFP=0.0776 +⌛️ [2/4] FRONTEND: Frontend time: 32.891s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.090s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00082351 5.49636479 + layer.9.1 0.02968625 5.28384644 + layer.19.0 0.00841222 25.13088540 + layer.19.1 0.03743129 24.79748690 + layer.29.0 4.28408194 227.22044605 + layer.29.1 4.28564945 226.54212707 + layer.39.0 8.35370986 15538.03574911 + layer.39.1 8.52557915 14979.34611635 + ------------------------------------------------------------------------------------- + TOTAL 3.19067171 3878.98162776 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 677560 +BPFP 0.0538 bits/point +EBPFP 0.0538 equivalent bits/point +MSE 3878.981628 +---------------------- ---------------------------------------------------------- +Time: 64.785s Load: 0.803s, Pack+Encode: 32.891s, Decode+Unpack: 31.090s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3878.9816 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.803s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,100B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,924B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,816B, BPFP=0.0411 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 65,780B, BPFP=0.0418 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 135,628B, BPFP=0.0861 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 136,388B, BPFP=0.0866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 152,296B, BPFP=0.0967 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 152,016B, BPFP=0.0965 +⌛️ [2/4] FRONTEND: Frontend time: 32.461s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.271s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14529990 5.26292668 + layer.9.1 0.14524076 5.22876794 + layer.19.0 0.03780325 22.88060357 + layer.19.1 0.03783790 24.88167757 + layer.29.0 4.32098184 239.57257475 + layer.29.1 4.32100596 238.96416965 + layer.39.0 9.32673680 15509.32206695 + layer.39.1 9.31823369 15448.93207670 + ------------------------------------------------------------------------------------- + TOTAL 3.45664251 3936.88060797 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 784948 +BPFP 0.0623 bits/point +EBPFP 0.0623 equivalent bits/point +MSE 3936.880608 +---------------------- ---------------------------------------------------------- +Time: 64.535s Load: 0.803s, Pack+Encode: 32.461s, Decode+Unpack: 31.271s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3936.8806 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.794s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,588B, BPFP=0.0251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,564B, BPFP=0.0251 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,216B, BPFP=0.0427 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,680B, BPFP=0.0430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 142,404B, BPFP=0.0904 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 141,804B, BPFP=0.0900 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 147,512B, BPFP=0.0936 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 150,348B, BPFP=0.0954 +⌛️ [2/4] FRONTEND: Frontend time: 31.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.223s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497286 5.23259421 + layer.9.1 0.14497296 5.22497309 + layer.19.0 0.03962668 24.69927334 + layer.19.1 0.11751332 25.11109644 + layer.29.0 0.14529291 225.65254306 + layer.29.1 0.16241527 223.59339454 + layer.39.0 11.40179406 16142.94702632 + layer.39.1 13.03458244 16081.90705232 + ------------------------------------------------------------------------------------- + TOTAL 3.14889631 4091.79599417 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 796116 +BPFP 0.0632 bits/point +EBPFP 0.0632 equivalent bits/point +MSE 4091.795994 +---------------------- ---------------------------------------------------------- +Time: 63.621s Load: 0.794s, Pack+Encode: 31.603s, Decode+Unpack: 31.223s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4091.7960 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.865s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,760B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,680B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,816B, BPFP=0.0392 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,312B, BPFP=0.0396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 130,256B, BPFP=0.0827 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 130,236B, BPFP=0.0827 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,672B, BPFP=0.0975 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 157,792B, BPFP=0.1002 +⌛️ [2/4] FRONTEND: Frontend time: 32.898s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.835s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14513344 5.25960821 + layer.9.1 0.03283094 5.21856180 + layer.19.0 0.11544709 25.28122461 + layer.19.1 0.11326018 24.63733192 + layer.29.0 0.14483232 228.08839779 + layer.29.1 0.14672551 229.41887390 + layer.39.0 10.02784076 15997.57036074 + layer.39.1 15.62606130 15495.67110822 + ------------------------------------------------------------------------------------- + TOTAL 3.29401644 4001.39318340 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 771524 +BPFP 0.0612 bits/point +EBPFP 0.0612 equivalent bits/point +MSE 4001.393183 +---------------------- ---------------------------------------------------------- +Time: 64.599s Load: 0.865s, Pack+Encode: 32.898s, Decode+Unpack: 30.835s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4001.3932 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.811s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,696B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,352B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,400B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,648B, BPFP=0.0398 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,884B, BPFP=0.0786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,688B, BPFP=0.0798 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 158,512B, BPFP=0.1006 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 163,852B, BPFP=0.1040 +⌛️ [2/4] FRONTEND: Frontend time: 32.706s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.258s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492303 5.31552776 + layer.9.1 0.14484742 5.00045226 + layer.19.0 0.11740684 25.27792391 + layer.19.1 0.11489933 23.70496324 + layer.29.0 0.12072669 223.40682889 + layer.29.1 0.12118037 222.40376178 + layer.39.0 10.74778980 16457.55996100 + layer.39.1 11.83662176 16980.29379266 + ------------------------------------------------------------------------------------- + TOTAL 2.91854940 4242.87040144 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 774032 +BPFP 0.0614 bits/point +EBPFP 0.0614 equivalent bits/point +MSE 4242.870401 +---------------------- ---------------------------------------------------------- +Time: 64.775s Load: 0.811s, Pack+Encode: 32.706s, Decode+Unpack: 31.258s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4242.8704 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.801s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,192B, BPFP=0.0242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,600B, BPFP=0.0245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,820B, BPFP=0.0424 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,280B, BPFP=0.0421 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 129,228B, BPFP=0.0820 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 131,364B, BPFP=0.0834 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 158,344B, BPFP=0.1005 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 155,436B, BPFP=0.0987 +⌛️ [2/4] FRONTEND: Frontend time: 31.894s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.154s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14465000 5.11098409 + layer.9.1 0.14489275 5.26779648 + layer.19.0 0.11978787 24.99752701 + layer.19.1 0.12819003 23.49725788 + layer.29.0 0.12519148 229.99037212 + layer.29.1 0.13018718 216.86587992 + layer.39.0 10.77894586 15972.27689308 + layer.39.1 10.25834823 16229.45726357 + ------------------------------------------------------------------------------------- + TOTAL 2.72877418 4088.43299677 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 784264 +BPFP 0.0622 bits/point +EBPFP 0.0622 equivalent bits/point +MSE 4088.432997 +---------------------- ---------------------------------------------------------- +Time: 63.848s Load: 0.801s, Pack+Encode: 31.894s, Decode+Unpack: 31.154s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4088.4330 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.931s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,152B, BPFP=0.0255 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,412B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,728B, BPFP=0.0405 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,792B, BPFP=0.0411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 116,924B, BPFP=0.0742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 117,720B, BPFP=0.0747 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 112,500B, BPFP=0.0714 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 113,384B, BPFP=0.0720 +⌛️ [2/4] FRONTEND: Frontend time: 31.794s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.184s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14553812 5.42373418 + layer.9.1 0.14559401 5.44626653 + layer.19.0 0.04492324 24.65679081 + layer.19.1 0.04213941 25.02271642 + layer.29.0 4.25320263 213.23740656 + layer.29.1 4.25391672 202.72519906 + layer.39.0 8.72311137 15682.58303542 + layer.39.1 8.87262096 15661.92525187 + ------------------------------------------------------------------------------------- + TOTAL 3.31013081 3977.62755011 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 668612 +BPFP 0.0531 bits/point +EBPFP 0.0531 equivalent bits/point +MSE 3977.627550 +---------------------- ---------------------------------------------------------- +Time: 63.909s Load: 0.931s, Pack+Encode: 31.794s, Decode+Unpack: 31.184s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3977.6276 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.917s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,964B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,216B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,600B, BPFP=0.0397 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,704B, BPFP=0.0398 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 124,356B, BPFP=0.0789 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 123,144B, BPFP=0.0782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 131,224B, BPFP=0.0833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 129,956B, BPFP=0.0825 +⌛️ [2/4] FRONTEND: Frontend time: 32.077s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 30.963s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14518122 5.33265457 + layer.9.1 0.14529820 5.33982788 + layer.19.0 0.11833418 24.46336224 + layer.19.1 0.12038008 24.74066918 + layer.29.0 4.31360161 226.56306874 + layer.29.1 4.31792870 228.06690770 + layer.39.0 9.40764201 15398.42963926 + layer.39.1 11.30764416 15700.33929152 + ------------------------------------------------------------------------------------- + TOTAL 3.73450127 3951.65942764 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 712164 +BPFP 0.0565 bits/point +EBPFP 0.0565 equivalent bits/point +MSE 3951.659428 +---------------------- ---------------------------------------------------------- +Time: 63.957s Load: 0.917s, Pack+Encode: 32.077s, Decode+Unpack: 30.963s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3951.6594 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.860s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,488B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,208B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,056B, BPFP=0.0413 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,892B, BPFP=0.0412 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,816B, BPFP=0.0735 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 117,724B, BPFP=0.0747 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 123,748B, BPFP=0.0785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 124,556B, BPFP=0.0791 +⌛️ [2/4] FRONTEND: Frontend time: 33.023s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.107s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00315138 5.30463767 + layer.9.1 0.00505826 5.12682998 + layer.19.0 0.09147678 23.04467623 + layer.19.1 0.09143778 23.22794118 + layer.29.0 0.11015094 227.78501787 + layer.29.1 0.11338039 224.04068492 + layer.39.0 9.14784464 15774.12414690 + layer.39.1 8.98944348 15665.91745206 + ------------------------------------------------------------------------------------- + TOTAL 2.31899296 3993.57142335 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 689488 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 3993.571423 +---------------------- ---------------------------------------------------------- +Time: 64.990s Load: 0.860s, Pack+Encode: 33.023s, Decode+Unpack: 31.107s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3993.5714 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.853s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,576B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,392B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,080B, BPFP=0.0426 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,036B, BPFP=0.0419 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,748B, BPFP=0.0785 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,984B, BPFP=0.0781 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 163,032B, BPFP=0.1035 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 162,296B, BPFP=0.1030 +⌛️ [2/4] FRONTEND: Frontend time: 32.824s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.100s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14591913 5.05499059 + layer.9.1 0.03347605 5.16641615 + layer.19.0 0.12173996 23.49278416 + layer.19.1 0.12099332 24.05352718 + layer.29.0 0.11078974 232.79929721 + layer.29.1 0.11776269 235.47146165 + layer.39.0 10.17800795 15450.82482938 + layer.39.1 9.88744998 15431.65680858 + ------------------------------------------------------------------------------------- + TOTAL 2.58951735 3926.06501436 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 783144 +BPFP 0.0621 bits/point +EBPFP 0.0621 equivalent bits/point +MSE 3926.065014 +---------------------- ---------------------------------------------------------- +Time: 64.776s Load: 0.853s, Pack+Encode: 32.824s, Decode+Unpack: 31.100s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3926.0650 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.808s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,240B, BPFP=0.0236 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,176B, BPFP=0.0236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,888B, BPFP=0.0406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,052B, BPFP=0.0394 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 134,256B, BPFP=0.0852 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 135,912B, BPFP=0.0863 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,432B, BPFP=0.0898 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 143,292B, BPFP=0.0910 +⌛️ [2/4] FRONTEND: Frontend time: 32.711s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66405266 5.34223993 + layer.9.1 2.66543197 5.57506551 + layer.19.0 3.22131407 23.30754133 + layer.19.1 3.22426883 24.11153569 + layer.29.0 4.27224607 233.47044605 + layer.29.1 4.27784520 231.43857654 + layer.39.0 8.94937744 15439.37341566 + layer.39.1 8.82170070 15347.04192395 + ------------------------------------------------------------------------------------- + TOTAL 4.76202962 3913.70759308 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 755248 +BPFP 0.0599 bits/point +EBPFP 0.0599 equivalent bits/point +MSE 3913.707593 +---------------------- ---------------------------------------------------------- +Time: 64.962s Load: 0.808s, Pack+Encode: 32.711s, Decode+Unpack: 31.443s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3913.7076 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.861s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,984B, BPFP=0.0235 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,368B, BPFP=0.0237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 56,908B, BPFP=0.0361 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,236B, BPFP=0.0363 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 116,296B, BPFP=0.0738 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,340B, BPFP=0.0726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,968B, BPFP=0.0774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 124,348B, BPFP=0.0789 +⌛️ [2/4] FRONTEND: Frontend time: 33.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00104424 5.40661117 + layer.9.1 0.00091568 5.24807988 + layer.19.0 0.08171424 24.68369658 + layer.19.1 0.08373584 24.74521906 + layer.29.0 4.26071267 221.11774862 + layer.29.1 4.26438533 219.98444101 + layer.39.0 8.39843369 16293.90705232 + layer.39.1 8.51949380 16228.93727657 + ------------------------------------------------------------------------------------- + TOTAL 3.20130444 4128.00376565 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 665448 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 4128.003766 +---------------------- ---------------------------------------------------------- +Time: 65.076s Load: 0.861s, Pack+Encode: 33.153s, Decode+Unpack: 31.063s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4128.0038 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.858s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,184B, BPFP=0.0249 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,004B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,636B, BPFP=0.0429 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,456B, BPFP=0.0428 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,984B, BPFP=0.0781 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 123,220B, BPFP=0.0782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,488B, BPFP=0.0771 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 121,588B, BPFP=0.0772 +⌛️ [2/4] FRONTEND: Frontend time: 32.992s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.358s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14622840 5.10238894 + layer.9.1 0.03344178 5.15798985 + layer.19.0 0.12675888 23.82969664 + layer.19.1 0.12382618 23.61721797 + layer.29.0 0.12223263 235.32568248 + layer.29.1 0.12797405 235.39124959 + layer.39.0 10.69978368 16811.92070198 + layer.39.1 8.63538768 16871.26941826 + ------------------------------------------------------------------------------------- + TOTAL 2.50195416 4276.45179322 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 702560 +BPFP 0.0557 bits/point +EBPFP 0.0557 equivalent bits/point +MSE 4276.451793 +---------------------- ---------------------------------------------------------- +Time: 65.208s Load: 0.858s, Pack+Encode: 32.992s, Decode+Unpack: 31.358s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4276.4518 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.850s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,416B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,508B, BPFP=0.0251 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,200B, BPFP=0.0427 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,236B, BPFP=0.0433 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 125,144B, BPFP=0.0794 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 126,440B, BPFP=0.0803 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 132,340B, BPFP=0.0840 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 132,952B, BPFP=0.0844 +⌛️ [2/4] FRONTEND: Frontend time: 31.447s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.179s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492994 5.04798645 + layer.9.1 0.14498602 4.97558625 + layer.19.0 0.12957112 23.90660038 + layer.19.1 0.13054295 24.41420468 + layer.29.0 0.16610158 219.89171677 + layer.29.1 0.14872770 225.54096929 + layer.39.0 16.52878844 16227.66330842 + layer.39.1 24.55764797 16313.18037049 + ------------------------------------------------------------------------------------- + TOTAL 5.24391197 4130.57759284 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 731236 +BPFP 0.0580 bits/point +EBPFP 0.0580 equivalent bits/point +MSE 4130.577593 +---------------------- ---------------------------------------------------------- +Time: 63.476s Load: 0.850s, Pack+Encode: 31.447s, Decode+Unpack: 31.179s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4130.5776 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0563 bits/point +Avg EBPFP 0.0563 equivalent bits/point +Avg MSE 4074.352778 +Avg Time 64.732s +------------------------ ---------------------------- diff --git a/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..32970360ab4655b536fec80870c39e770d7a41d5 --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 255 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,216B, BPFP=0.1004 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 164,612B, BPFP=0.1045 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 389,992B, BPFP=0.2475 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 388,972B, BPFP=0.2469 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 697,936B, BPFP=0.4430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 689,732B, BPFP=0.4378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,980B, BPFP=0.3066 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 476,560B, BPFP=0.3025 +⌛️ [2/4] FRONTEND: Frontend time: 32.678s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.286s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561056 4.36633598 + layer.9.1 0.14522085 4.36365448 + layer.19.0 3.25142184 6.53309522 + layer.19.1 3.25206135 6.79355018 + layer.29.0 4.23946030 39.52154595 + layer.29.1 4.24539299 39.10623680 + layer.39.0 32.17105490 1808.73724407 + layer.39.1 19.15684032 1843.02096198 + ------------------------------------------------------------------------------------- + TOTAL 8.32588289 469.05532808 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3449000 +BPFP 0.2737 bits/point +EBPFP 0.2737 equivalent bits/point +MSE 469.055328 +---------------------- ---------------------------------------------------------- +Time: 65.171s Load: 1.206s, Pack+Encode: 32.678s, Decode+Unpack: 31.286s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 469.0553 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.187s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 161,968B, BPFP=0.1028 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 158,724B, BPFP=0.1008 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 483,732B, BPFP=0.3070 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 470,360B, BPFP=0.2986 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 904,576B, BPFP=0.5742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 869,288B, BPFP=0.5518 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 534,104B, BPFP=0.3390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 525,820B, BPFP=0.3338 +⌛️ [2/4] FRONTEND: Frontend time: 33.116s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.302s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03307773 4.37967017 + layer.9.1 0.03291117 4.37869012 + layer.19.0 0.04156009 7.04850821 + layer.19.1 0.03760627 7.01179302 + layer.29.0 4.28582750 53.79708828 + layer.29.1 4.28551552 52.02742627 + layer.39.0 9.83402183 2009.98115047 + layer.39.1 9.85397836 2044.40965226 + ------------------------------------------------------------------------------------- + TOTAL 3.55056231 522.87924735 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4108572 +BPFP 0.3260 bits/point +EBPFP 0.3260 equivalent bits/point +MSE 522.879247 +---------------------- ---------------------------------------------------------- +Time: 65.604s Load: 1.187s, Pack+Encode: 33.116s, Decode+Unpack: 31.302s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 522.8792 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.194s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,344B, BPFP=0.1113 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 177,888B, BPFP=0.1129 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 514,848B, BPFP=0.3268 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 517,468B, BPFP=0.3285 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,008,560B, BPFP=0.6402 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 989,856B, BPFP=0.6283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,120B, BPFP=0.3384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 531,764B, BPFP=0.3375 +⌛️ [2/4] FRONTEND: Frontend time: 33.021s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083900 4.34149664 + layer.9.1 0.00259629 4.40222124 + layer.19.0 0.00955961 6.99552184 + layer.19.1 0.08538111 7.30663174 + layer.29.0 0.11631418 58.56834478 + layer.29.1 0.11200302 48.53134140 + layer.39.0 14.47657393 2655.46457589 + layer.39.1 13.08093694 2645.82596685 + ------------------------------------------------------------------------------------- + TOTAL 3.48552551 678.92951255 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4448848 +BPFP 0.3530 bits/point +EBPFP 0.3530 equivalent bits/point +MSE 678.929513 +---------------------- ---------------------------------------------------------- +Time: 65.828s Load: 1.194s, Pack+Encode: 33.021s, Decode+Unpack: 31.612s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.9295 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.201s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,172B, BPFP=0.1004 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 157,624B, BPFP=0.1001 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 428,776B, BPFP=0.2722 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 440,124B, BPFP=0.2794 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 792,056B, BPFP=0.5028 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 789,960B, BPFP=0.5014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,348B, BPFP=0.3087 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 493,772B, BPFP=0.3134 +⌛️ [2/4] FRONTEND: Frontend time: 33.124s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.581s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.70169554 4.36959860 + layer.9.1 0.03294074 4.35256223 + layer.19.0 3.25671692 6.66744762 + layer.19.1 3.25834093 6.72680688 + layer.29.0 0.10810242 41.44178075 + layer.29.1 0.10661203 40.39101347 + layer.39.0 8.95005916 1811.03152421 + layer.39.1 8.98756017 1907.80337992 + ------------------------------------------------------------------------------------- + TOTAL 3.42525349 477.84801421 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3746832 +BPFP 0.2973 bits/point +EBPFP 0.2973 equivalent bits/point +MSE 477.848014 +---------------------- ---------------------------------------------------------- +Time: 65.907s Load: 1.201s, Pack+Encode: 33.124s, Decode+Unpack: 31.581s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 477.8480 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,664B, BPFP=0.1172 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,124B, BPFP=0.1169 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 484,048B, BPFP=0.3072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 482,896B, BPFP=0.3065 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 936,440B, BPFP=0.5944 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 944,180B, BPFP=0.5993 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 503,148B, BPFP=0.3194 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 516,604B, BPFP=0.3279 +⌛️ [2/4] FRONTEND: Frontend time: 33.101s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527870 4.37785796 + layer.9.1 0.14521496 4.37098299 + layer.19.0 0.03964342 7.22174856 + layer.19.1 0.03956446 7.67280681 + layer.29.0 0.12258449 67.19558925 + layer.29.1 0.12735008 65.61498619 + layer.39.0 32.94776263 2397.99415015 + layer.39.1 29.25669534 2357.44491388 + ------------------------------------------------------------------------------------- + TOTAL 7.85301176 613.98662947 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4236104 +BPFP 0.3361 bits/point +EBPFP 0.3361 equivalent bits/point +MSE 613.986629 +---------------------- ---------------------------------------------------------- +Time: 65.813s Load: 1.209s, Pack+Encode: 33.101s, Decode+Unpack: 31.502s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 613.9866 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.210s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 167,732B, BPFP=0.1065 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,648B, BPFP=0.1109 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 431,768B, BPFP=0.2741 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 428,832B, BPFP=0.2722 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 803,736B, BPFP=0.5102 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 800,236B, BPFP=0.5079 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,344B, BPFP=0.3024 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 482,412B, BPFP=0.3062 +⌛️ [2/4] FRONTEND: Frontend time: 33.028s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.172s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66816044 4.42385796 + layer.9.1 2.66817504 4.43522474 + layer.19.0 3.22262959 6.85473434 + layer.19.1 3.22037432 6.98330799 + layer.29.0 4.30448692 53.24137248 + layer.29.1 4.31085282 54.32477860 + layer.39.0 38.33931691 2185.38430289 + layer.39.1 57.25219370 2271.52144946 + ------------------------------------------------------------------------------------- + TOTAL 14.49827372 573.39612856 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3765708 +BPFP 0.2988 bits/point +EBPFP 0.2988 equivalent bits/point +MSE 573.396129 +---------------------- ---------------------------------------------------------- +Time: 65.410s Load: 1.210s, Pack+Encode: 33.028s, Decode+Unpack: 31.172s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 573.3961 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.199s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,412B, BPFP=0.1082 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 167,568B, BPFP=0.1064 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 435,408B, BPFP=0.2764 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 431,416B, BPFP=0.2738 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 798,964B, BPFP=0.5071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 800,932B, BPFP=0.5084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,880B, BPFP=0.2970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 468,752B, BPFP=0.2975 +⌛️ [2/4] FRONTEND: Frontend time: 33.195s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.417s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332920 4.45065678 + layer.9.1 0.00092169 4.45423232 + layer.19.0 3.23006092 6.84190859 + layer.19.1 3.23257961 7.27112066 + layer.29.0 4.28548854 45.39283901 + layer.29.1 4.27808990 42.06451343 + layer.39.0 10.57841825 1754.90185245 + layer.39.1 20.33118703 1738.06142346 + ------------------------------------------------------------------------------------- + TOTAL 5.75625939 450.42981834 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3741332 +BPFP 0.2969 bits/point +EBPFP 0.2969 equivalent bits/point +MSE 450.429818 +---------------------- ---------------------------------------------------------- +Time: 65.811s Load: 1.199s, Pack+Encode: 33.195s, Decode+Unpack: 31.417s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 450.4298 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,880B, BPFP=0.1091 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,260B, BPFP=0.1112 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 435,832B, BPFP=0.2766 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 441,624B, BPFP=0.2803 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 740,360B, BPFP=0.4699 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 758,364B, BPFP=0.4814 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 448,760B, BPFP=0.2849 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 449,600B, BPFP=0.2854 +⌛️ [2/4] FRONTEND: Frontend time: 33.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.690s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14443954 4.33087283 + layer.9.1 0.14435121 4.34220756 + layer.19.0 0.03807715 7.92074959 + layer.19.1 0.03781311 7.35277614 + layer.29.0 0.10781899 43.20461285 + layer.29.1 0.10618912 43.61056325 + layer.39.0 9.30898666 2011.03721157 + layer.39.1 9.83625107 2046.37049074 + ------------------------------------------------------------------------------------- + TOTAL 2.46549086 521.02118557 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3621680 +BPFP 0.2874 bits/point +EBPFP 0.2874 equivalent bits/point +MSE 521.021186 +---------------------- ---------------------------------------------------------- +Time: 66.189s Load: 1.208s, Pack+Encode: 33.291s, Decode+Unpack: 31.690s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 521.0212 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.224s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,776B, BPFP=0.1097 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 172,944B, BPFP=0.1098 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 500,356B, BPFP=0.3176 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 504,752B, BPFP=0.3204 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 972,452B, BPFP=0.6173 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 985,816B, BPFP=0.6257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 506,480B, BPFP=0.3215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 495,620B, BPFP=0.3146 +⌛️ [2/4] FRONTEND: Frontend time: 33.179s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.643s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14543928 4.40200479 + layer.9.1 0.14562574 4.39637678 + layer.19.0 0.11552505 7.45827599 + layer.19.1 0.12052174 7.55606998 + layer.29.0 0.10841144 51.63134750 + layer.29.1 0.10845811 53.40501605 + layer.39.0 9.17501701 2170.57556061 + layer.39.1 9.20635778 2149.63015925 + ------------------------------------------------------------------------------------- + TOTAL 2.39066952 556.13185137 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4311196 +BPFP 0.3421 bits/point +EBPFP 0.3421 equivalent bits/point +MSE 556.131851 +---------------------- ---------------------------------------------------------- +Time: 66.046s Load: 1.224s, Pack+Encode: 33.179s, Decode+Unpack: 31.643s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 556.1319 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,404B, BPFP=0.1132 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,440B, BPFP=0.1114 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 399,844B, BPFP=0.2538 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 396,140B, BPFP=0.2514 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 680,460B, BPFP=0.4319 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 691,180B, BPFP=0.4387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,936B, BPFP=0.2856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 460,656B, BPFP=0.2924 +⌛️ [2/4] FRONTEND: Frontend time: 33.234s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.347s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.78226592 4.44755317 + layer.9.1 2.78427046 4.43527711 + layer.19.0 3.22580366 6.53246999 + layer.19.1 3.22969594 6.90167663 + layer.29.0 4.29525448 40.07992515 + layer.29.1 0.11349234 36.95121567 + layer.39.0 8.89338553 1774.58888528 + layer.39.1 8.88767087 1777.53753656 + ------------------------------------------------------------------------------------- + TOTAL 4.27647990 456.43431744 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3432060 +BPFP 0.2723 bits/point +EBPFP 0.2723 equivalent bits/point +MSE 456.434317 +---------------------- ---------------------------------------------------------- +Time: 65.794s Load: 1.213s, Pack+Encode: 33.234s, Decode+Unpack: 31.347s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 456.4343 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.200s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,508B, BPFP=0.1165 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 180,800B, BPFP=0.1148 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 443,052B, BPFP=0.2812 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 433,492B, BPFP=0.2752 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 761,572B, BPFP=0.4834 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 754,140B, BPFP=0.4787 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 430,776B, BPFP=0.2734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 432,764B, BPFP=0.2747 +⌛️ [2/4] FRONTEND: Frontend time: 33.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14514284 4.36422861 + layer.9.1 0.14518188 4.39139114 + layer.19.0 0.04057091 6.94397254 + layer.19.1 0.04041447 8.02442009 + layer.29.0 4.25641542 48.16745206 + layer.29.1 4.26613502 44.91385684 + layer.39.0 12.58558458 1800.33669158 + layer.39.1 8.96866240 1923.94881378 + ------------------------------------------------------------------------------------- + TOTAL 3.80601344 480.13635333 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3620104 +BPFP 0.2872 bits/point +EBPFP 0.2872 equivalent bits/point +MSE 480.136353 +---------------------- ---------------------------------------------------------- +Time: 65.929s Load: 1.200s, Pack+Encode: 33.293s, Decode+Unpack: 31.436s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 480.1364 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.210s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,084B, BPFP=0.1137 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,656B, BPFP=0.1090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 425,948B, BPFP=0.2704 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 423,168B, BPFP=0.2686 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 743,928B, BPFP=0.4722 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 745,732B, BPFP=0.4734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,184B, BPFP=0.2832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 457,388B, BPFP=0.2903 +⌛️ [2/4] FRONTEND: Frontend time: 33.228s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.660s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67015388 4.45586236 + layer.9.1 0.00076871 4.42399919 + layer.19.0 3.22151687 6.97366931 + layer.19.1 3.22388957 6.78854581 + layer.29.0 4.24084786 43.63756297 + layer.29.1 4.24602234 40.47636192 + layer.39.0 7.87160790 1642.86935327 + layer.39.1 9.85764150 1709.03981150 + ------------------------------------------------------------------------------------- + TOTAL 4.41655608 432.33314579 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3593088 +BPFP 0.2851 bits/point +EBPFP 0.2851 equivalent bits/point +MSE 432.333146 +---------------------- ---------------------------------------------------------- +Time: 66.098s Load: 1.210s, Pack+Encode: 33.228s, Decode+Unpack: 31.660s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 432.3331 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,448B, BPFP=0.1095 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 179,736B, BPFP=0.1141 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 503,392B, BPFP=0.3195 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 514,240B, BPFP=0.3264 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,024,620B, BPFP=0.6504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,010,320B, BPFP=0.6413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 551,676B, BPFP=0.3502 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 558,560B, BPFP=0.3545 +⌛️ [2/4] FRONTEND: Frontend time: 32.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00068237 4.43331954 + layer.9.1 0.00070576 4.42429816 + layer.19.0 0.00823322 7.37943563 + layer.19.1 0.08594799 7.32164992 + layer.29.0 0.12200666 63.26506134 + layer.29.1 0.12451052 56.52447087 + layer.39.0 55.99513528 2730.22814430 + layer.39.1 28.81185256 2673.36041599 + ------------------------------------------------------------------------------------- + TOTAL 10.64363429 693.36709947 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4514992 +BPFP 0.3582 bits/point +EBPFP 0.3582 equivalent bits/point +MSE 693.367099 +---------------------- ---------------------------------------------------------- +Time: 65.313s Load: 1.203s, Pack+Encode: 32.631s, Decode+Unpack: 31.480s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 693.3671 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.210s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,300B, BPFP=0.1170 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 182,128B, BPFP=0.1156 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 458,676B, BPFP=0.2911 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 461,172B, BPFP=0.2927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 760,068B, BPFP=0.4825 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 770,344B, BPFP=0.4890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,920B, BPFP=0.2983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 480,068B, BPFP=0.3047 +⌛️ [2/4] FRONTEND: Frontend time: 32.090s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.264s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14556937 4.39794684 + layer.9.1 0.03327741 4.39769326 + layer.19.0 0.11590617 7.17576919 + layer.19.1 0.11733878 6.91479691 + layer.29.0 0.11334742 42.22673769 + layer.29.1 4.29039579 42.88005261 + layer.39.0 9.10722066 1825.55963601 + layer.39.1 44.52401893 1881.49918752 + ------------------------------------------------------------------------------------- + TOTAL 7.30588432 476.88147750 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3766676 +BPFP 0.2989 bits/point +EBPFP 0.2989 equivalent bits/point +MSE 476.881478 +---------------------- ---------------------------------------------------------- +Time: 64.564s Load: 1.210s, Pack+Encode: 32.090s, Decode+Unpack: 31.264s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 476.8815 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,212B, BPFP=0.1106 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 176,408B, BPFP=0.1120 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 483,168B, BPFP=0.3067 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 482,576B, BPFP=0.3063 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 900,232B, BPFP=0.5714 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 926,016B, BPFP=0.5878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,084B, BPFP=0.2724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 436,652B, BPFP=0.2772 +⌛️ [2/4] FRONTEND: Frontend time: 33.018s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.376s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11318631 4.43827376 + layer.9.1 0.11319129 4.43735782 + layer.19.0 0.00665199 7.83758658 + layer.19.1 0.00853768 7.80283086 + layer.29.0 4.27225940 76.09358750 + layer.29.1 4.27324961 74.24505911 + layer.39.0 14.80262837 1720.40103997 + layer.39.1 16.56649765 1755.04582385 + ------------------------------------------------------------------------------------- + TOTAL 5.01952529 456.28769493 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4008348 +BPFP 0.3180 bits/point +EBPFP 0.3180 equivalent bits/point +MSE 456.287695 +---------------------- ---------------------------------------------------------- +Time: 65.607s Load: 1.213s, Pack+Encode: 33.018s, Decode+Unpack: 31.376s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 456.2877 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,048B, BPFP=0.1092 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 170,616B, BPFP=0.1083 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 468,232B, BPFP=0.2972 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 458,168B, BPFP=0.2908 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 859,856B, BPFP=0.5458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 841,564B, BPFP=0.5342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,032B, BPFP=0.2990 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 469,424B, BPFP=0.2980 +⌛️ [2/4] FRONTEND: Frontend time: 33.098s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00065117 4.43382766 + layer.9.1 0.00066201 4.41577696 + layer.19.0 0.00984582 6.90600245 + layer.19.1 0.01156107 6.96361234 + layer.29.0 4.26547583 61.83013081 + layer.29.1 4.26296603 51.27389198 + layer.39.0 11.21169412 1890.97351316 + layer.39.1 9.31977106 1933.83474163 + ------------------------------------------------------------------------------------- + TOTAL 3.63532839 495.07893712 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3910940 +BPFP 0.3103 bits/point +EBPFP 0.3103 equivalent bits/point +MSE 495.078937 +---------------------- ---------------------------------------------------------- +Time: 65.779s Load: 1.206s, Pack+Encode: 33.098s, Decode+Unpack: 31.475s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 495.0789 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.201s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,876B, BPFP=0.1008 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 158,376B, BPFP=0.1005 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 407,612B, BPFP=0.2587 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 400,800B, BPFP=0.2544 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 758,456B, BPFP=0.4814 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 742,480B, BPFP=0.4713 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 433,472B, BPFP=0.2751 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 431,116B, BPFP=0.2737 +⌛️ [2/4] FRONTEND: Frontend time: 33.105s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.582s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00085355 4.45795418 + layer.9.1 0.00085581 4.47154385 + layer.19.0 0.00808159 6.62847335 + layer.19.1 0.00635426 6.59960684 + layer.29.0 4.24551200 46.16844735 + layer.29.1 4.24803037 43.42216445 + layer.39.0 9.19283951 1759.02794930 + layer.39.1 9.46657027 1660.91095223 + ------------------------------------------------------------------------------------- + TOTAL 3.39613717 441.46088644 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3491188 +BPFP 0.2770 bits/point +EBPFP 0.2770 equivalent bits/point +MSE 441.460886 +---------------------- ---------------------------------------------------------- +Time: 65.888s Load: 1.201s, Pack+Encode: 33.105s, Decode+Unpack: 31.582s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 441.4609 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.207s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 164,616B, BPFP=0.1045 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,676B, BPFP=0.1090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 465,140B, BPFP=0.2952 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 469,768B, BPFP=0.2982 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 886,500B, BPFP=0.5627 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 889,760B, BPFP=0.5648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,864B, BPFP=0.3071 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 492,624B, BPFP=0.3127 +⌛️ [2/4] FRONTEND: Frontend time: 33.180s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.571s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67056234 4.41673131 + layer.9.1 2.67147828 4.41851241 + layer.19.0 0.00618387 7.07273471 + layer.19.1 0.08383032 7.68175932 + layer.29.0 4.28489822 53.02631419 + layer.29.1 4.28470970 47.78691705 + layer.39.0 10.15376305 1874.14397140 + layer.39.1 8.47863686 1866.09912252 + ------------------------------------------------------------------------------------- + TOTAL 4.07925783 483.08075786 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4023948 +BPFP 0.3193 bits/point +EBPFP 0.3193 equivalent bits/point +MSE 483.080758 +---------------------- ---------------------------------------------------------- +Time: 65.959s Load: 1.207s, Pack+Encode: 33.180s, Decode+Unpack: 31.571s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 483.0808 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,592B, BPFP=0.1070 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 169,252B, BPFP=0.1074 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 483,532B, BPFP=0.3069 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 489,628B, BPFP=0.3108 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 927,608B, BPFP=0.5888 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 941,256B, BPFP=0.5975 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,292B, BPFP=0.3087 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 482,108B, BPFP=0.3060 +⌛️ [2/4] FRONTEND: Frontend time: 33.196s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.613s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67132404 4.42794797 + layer.9.1 2.67117709 4.41511968 + layer.19.0 0.00597838 7.05346942 + layer.19.1 0.00605309 7.04841236 + layer.29.0 4.29273040 59.77350097 + layer.29.1 4.29206328 65.62838195 + layer.39.0 9.96127074 1875.09246019 + layer.39.1 10.21295854 1866.18524537 + ------------------------------------------------------------------------------------- + TOTAL 4.26419445 486.20306724 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4148268 +BPFP 0.3291 bits/point +EBPFP 0.3291 equivalent bits/point +MSE 486.203067 +---------------------- ---------------------------------------------------------- +Time: 66.015s Load: 1.206s, Pack+Encode: 33.196s, Decode+Unpack: 31.613s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 486.2031 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.222s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,468B, BPFP=0.1101 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 172,660B, BPFP=0.1096 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 441,720B, BPFP=0.2804 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 441,228B, BPFP=0.2801 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 717,692B, BPFP=0.4556 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 717,896B, BPFP=0.4557 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 447,752B, BPFP=0.2842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 449,000B, BPFP=0.2850 +⌛️ [2/4] FRONTEND: Frontend time: 32.918s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568686 4.38540164 + layer.9.1 0.14558674 4.37863712 + layer.19.0 0.00960369 7.70485152 + layer.19.1 0.03847206 7.24485789 + layer.29.0 4.24438723 41.01434534 + layer.29.1 4.24578970 40.75531666 + layer.39.0 9.23757985 1737.14543386 + layer.39.1 9.43674592 1767.91127722 + ------------------------------------------------------------------------------------- + TOTAL 3.43798151 451.31751516 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3561416 +BPFP 0.2826 bits/point +EBPFP 0.2826 equivalent bits/point +MSE 451.317515 +---------------------- ---------------------------------------------------------- +Time: 65.672s Load: 1.222s, Pack+Encode: 32.918s, Decode+Unpack: 31.532s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 451.3175 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.195s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,408B, BPFP=0.1158 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 181,344B, BPFP=0.1151 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 523,848B, BPFP=0.3325 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 523,060B, BPFP=0.3320 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 961,972B, BPFP=0.6106 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 932,844B, BPFP=0.5921 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,396B, BPFP=0.3125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 495,064B, BPFP=0.3142 +⌛️ [2/4] FRONTEND: Frontend time: 33.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.663s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11307967 4.45059679 + layer.9.1 0.00073224 4.42294868 + layer.19.0 0.08207503 7.61941357 + layer.19.1 0.08214869 7.43626732 + layer.29.0 4.26728487 47.40640234 + layer.29.1 4.26774951 49.50493074 + layer.39.0 12.81553410 1973.01917452 + layer.39.1 23.05196315 1976.92248944 + ------------------------------------------------------------------------------------- + TOTAL 5.58507091 508.84777792 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4292936 +BPFP 0.3406 bits/point +EBPFP 0.3406 equivalent bits/point +MSE 508.847778 +---------------------- ---------------------------------------------------------- +Time: 66.153s Load: 1.195s, Pack+Encode: 33.295s, Decode+Unpack: 31.663s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 508.8478 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,232B, BPFP=0.1296 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 204,628B, BPFP=0.1299 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 617,364B, BPFP=0.3919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 624,744B, BPFP=0.3966 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,141,168B, BPFP=0.7244 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,165,824B, BPFP=0.7400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,228B, BPFP=0.3004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 469,920B, BPFP=0.2983 +⌛️ [2/4] FRONTEND: Frontend time: 33.113s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.635s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519844 4.40730591 + layer.9.1 0.14499054 4.40050932 + layer.19.0 0.12156012 8.20492325 + layer.19.1 0.12030756 26.43893453 + layer.29.0 0.12020218 57.56812642 + layer.29.1 0.12115470 58.02919341 + layer.39.0 8.85439666 2135.61358466 + layer.39.1 8.75438231 2121.55134872 + ------------------------------------------------------------------------------------- + TOTAL 2.29777406 552.02674078 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4901108 +BPFP 0.3889 bits/point +EBPFP 0.3889 equivalent bits/point +MSE 552.026741 +---------------------- ---------------------------------------------------------- +Time: 65.956s Load: 1.208s, Pack+Encode: 33.113s, Decode+Unpack: 31.635s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 552.0267 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.216s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,676B, BPFP=0.1382 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 213,680B, BPFP=0.1356 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 644,972B, BPFP=0.4094 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 647,204B, BPFP=0.4108 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,160,072B, BPFP=0.7364 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,170,196B, BPFP=0.7428 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,560B, BPFP=0.2803 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 438,160B, BPFP=0.2781 +⌛️ [2/4] FRONTEND: Frontend time: 33.027s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.294s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14541288 4.39486798 + layer.9.1 0.14479464 4.39992662 + layer.19.0 0.11855170 7.80996291 + layer.19.1 0.11778439 22.08352291 + layer.29.0 0.12648388 72.12756439 + layer.29.1 0.12520221 69.41816298 + layer.39.0 8.37129624 1839.62300942 + layer.39.1 8.45478741 1832.16525837 + ------------------------------------------------------------------------------------- + TOTAL 2.20053917 481.50278445 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4933520 +BPFP 0.3914 bits/point +EBPFP 0.3914 equivalent bits/point +MSE 481.502784 +---------------------- ---------------------------------------------------------- +Time: 65.537s Load: 1.216s, Pack+Encode: 33.027s, Decode+Unpack: 31.294s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 481.5028 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,572B, BPFP=0.1222 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 195,192B, BPFP=0.1239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 680,768B, BPFP=0.4321 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 678,056B, BPFP=0.4304 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,194,848B, BPFP=0.7584 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,192,256B, BPFP=0.7568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 545,268B, BPFP=0.3461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 549,284B, BPFP=0.3487 +⌛️ [2/4] FRONTEND: Frontend time: 32.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.111s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14458197 4.39060818 + layer.9.1 0.14461228 4.41324653 + layer.19.0 0.12127609 8.48088197 + layer.19.1 0.12505172 8.50099402 + layer.29.0 0.11568762 62.71178806 + layer.29.1 0.11796058 63.02751767 + layer.39.0 8.63782956 2301.01657459 + layer.39.1 8.69862780 2310.22603185 + ------------------------------------------------------------------------------------- + TOTAL 2.26320345 595.34595536 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5228244 +BPFP 0.4148 bits/point +EBPFP 0.4148 equivalent bits/point +MSE 595.345955 +---------------------- ---------------------------------------------------------- +Time: 64.958s Load: 1.203s, Pack+Encode: 32.644s, Decode+Unpack: 31.111s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 595.3460 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.156s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,640B, BPFP=0.1210 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 195,640B, BPFP=0.1242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 724,256B, BPFP=0.4597 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 710,464B, BPFP=0.4510 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,270,320B, BPFP=0.8063 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,247,724B, BPFP=0.7920 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 590,672B, BPFP=0.3749 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 582,904B, BPFP=0.3700 +⌛️ [2/4] FRONTEND: Frontend time: 33.127s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.638s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14478087 4.37936486 + layer.9.1 0.14472154 4.39811759 + layer.19.0 0.13423899 8.53269659 + layer.19.1 0.13534726 8.42150050 + layer.29.0 0.11251127 59.90340632 + layer.29.1 0.11242151 59.87320747 + layer.39.0 10.58490794 2344.90770231 + layer.39.1 8.80008176 2312.97042574 + ------------------------------------------------------------------------------------- + TOTAL 2.52112639 600.42330267 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5512620 +BPFP 0.4374 bits/point +EBPFP 0.4374 equivalent bits/point +MSE 600.423303 +---------------------- ---------------------------------------------------------- +Time: 65.921s Load: 1.156s, Pack+Encode: 33.127s, Decode+Unpack: 31.638s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 600.4233 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.218s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 200,944B, BPFP=0.1275 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 202,296B, BPFP=0.1284 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 570,444B, BPFP=0.3621 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 573,620B, BPFP=0.3641 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,053,508B, BPFP=0.6687 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,060,348B, BPFP=0.6731 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 531,556B, BPFP=0.3374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 532,104B, BPFP=0.3378 +⌛️ [2/4] FRONTEND: Frontend time: 33.307s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.650s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14590075 4.41091668 + layer.9.1 0.14620647 4.42123581 + layer.19.0 0.11628058 7.62321953 + layer.19.1 0.11601873 7.73309407 + layer.29.0 0.11558260 53.38691806 + layer.29.1 0.11828149 50.72238077 + layer.39.0 28.43028163 2448.97075073 + layer.39.1 24.81181701 2394.38982775 + ------------------------------------------------------------------------------------- + TOTAL 6.75004616 621.45729293 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4724820 +BPFP 0.3749 bits/point +EBPFP 0.3749 equivalent bits/point +MSE 621.457293 +---------------------- ---------------------------------------------------------- +Time: 66.175s Load: 1.218s, Pack+Encode: 33.307s, Decode+Unpack: 31.650s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 621.4573 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,272B, BPFP=0.1170 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,260B, BPFP=0.1163 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 579,224B, BPFP=0.3677 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 579,032B, BPFP=0.3675 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,135,068B, BPFP=0.7205 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,123,136B, BPFP=0.7129 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 552,072B, BPFP=0.3504 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 532,820B, BPFP=0.3382 +⌛️ [2/4] FRONTEND: Frontend time: 33.072s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.547s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11529700 4.44263893 + layer.9.1 0.14629077 4.43210717 + layer.19.0 0.09721754 7.45763045 + layer.19.1 0.12446257 7.61074098 + layer.29.0 4.28687864 66.22189836 + layer.29.1 4.28715508 62.07259506 + layer.39.0 11.34089363 2327.41566461 + layer.39.1 19.75513766 2283.15843354 + ------------------------------------------------------------------------------------- + TOTAL 5.01916661 595.35146364 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4868884 +BPFP 0.3863 bits/point +EBPFP 0.3863 equivalent bits/point +MSE 595.351464 +---------------------- ---------------------------------------------------------- +Time: 65.822s Load: 1.203s, Pack+Encode: 33.072s, Decode+Unpack: 31.547s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 595.3515 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,244B, BPFP=0.1233 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 192,332B, BPFP=0.1221 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 559,432B, BPFP=0.3551 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 560,936B, BPFP=0.3561 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,031,248B, BPFP=0.6546 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,058,056B, BPFP=0.6716 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 602,480B, BPFP=0.3824 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 617,384B, BPFP=0.3919 +⌛️ [2/4] FRONTEND: Frontend time: 33.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.409s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14538718 4.41084305 + layer.9.1 0.14538559 4.40383129 + layer.19.0 0.11434236 7.48453496 + layer.19.1 0.11406084 7.60867931 + layer.29.0 0.11219077 53.39363117 + layer.29.1 0.11281304 57.12597498 + layer.39.0 79.88316542 2404.44637634 + layer.39.1 46.71980622 2552.26974326 + ------------------------------------------------------------------------------------- + TOTAL 15.91839393 636.39295179 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4816112 +BPFP 0.3821 bits/point +EBPFP 0.3821 equivalent bits/point +MSE 636.392952 +---------------------- ---------------------------------------------------------- +Time: 65.766s Load: 1.203s, Pack+Encode: 33.154s, Decode+Unpack: 31.409s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 636.3930 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,252B, BPFP=0.1290 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 202,608B, BPFP=0.1286 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 579,964B, BPFP=0.3681 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 581,624B, BPFP=0.3692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,034,560B, BPFP=0.6567 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,044,712B, BPFP=0.6631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 614,240B, BPFP=0.3899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 616,992B, BPFP=0.3916 +⌛️ [2/4] FRONTEND: Frontend time: 33.190s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.407s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14512126 4.39703724 + layer.9.1 0.14517278 4.40471391 + layer.19.0 0.11689420 7.59806757 + layer.19.1 0.12099910 7.53323677 + layer.29.0 0.11847120 65.65904290 + layer.29.1 0.12399357 66.08597051 + layer.39.0 75.86630139 2952.23464413 + layer.39.1 56.61936342 2722.16737082 + ------------------------------------------------------------------------------------- + TOTAL 16.65703962 728.76001048 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4877952 +BPFP 0.3870 bits/point +EBPFP 0.3870 equivalent bits/point +MSE 728.760010 +---------------------- ---------------------------------------------------------- +Time: 65.799s Load: 1.203s, Pack+Encode: 33.190s, Decode+Unpack: 31.407s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 728.7600 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,656B, BPFP=0.1178 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,456B, BPFP=0.1164 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 546,828B, BPFP=0.3471 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 546,320B, BPFP=0.3468 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 992,816B, BPFP=0.6302 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 989,320B, BPFP=0.6280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 639,300B, BPFP=0.4058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 634,336B, BPFP=0.4026 +⌛️ [2/4] FRONTEND: Frontend time: 33.079s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.642s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488649 4.42100285 + layer.9.1 0.14606862 4.42652359 + layer.19.0 0.08767178 7.22649585 + layer.19.1 0.11443626 7.36218059 + layer.29.0 0.10933029 50.59576089 + layer.29.1 0.10817130 50.33734664 + layer.39.0 52.66717785 2728.13877153 + layer.39.1 62.91127214 2801.29509262 + ------------------------------------------------------------------------------------- + TOTAL 14.53237684 706.72539682 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4718032 +BPFP 0.3743 bits/point +EBPFP 0.3743 equivalent bits/point +MSE 706.725397 +---------------------- ---------------------------------------------------------- +Time: 65.930s Load: 1.209s, Pack+Encode: 33.079s, Decode+Unpack: 31.642s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 706.7254 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,488B, BPFP=0.1165 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,628B, BPFP=0.1172 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 541,172B, BPFP=0.3435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 534,956B, BPFP=0.3396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 933,100B, BPFP=0.5923 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 933,644B, BPFP=0.5926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 564,984B, BPFP=0.3586 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 574,284B, BPFP=0.3645 +⌛️ [2/4] FRONTEND: Frontend time: 33.240s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.653s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509552 4.39927156 + layer.9.1 0.14520687 4.40679493 + layer.19.0 0.12118574 7.47304091 + layer.19.1 0.11709642 7.27930576 + layer.29.0 0.10963326 54.19356821 + layer.29.1 0.10842036 54.06472416 + layer.39.0 53.79489966 2545.05297368 + layer.39.1 62.27410526 2470.00584985 + ------------------------------------------------------------------------------------- + TOTAL 14.60195539 643.35944113 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4450256 +BPFP 0.3531 bits/point +EBPFP 0.3531 equivalent bits/point +MSE 643.359441 +---------------------- ---------------------------------------------------------- +Time: 66.101s Load: 1.208s, Pack+Encode: 33.240s, Decode+Unpack: 31.653s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 643.3594 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,880B, BPFP=0.1339 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 209,484B, BPFP=0.1330 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 630,196B, BPFP=0.4000 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 627,744B, BPFP=0.3985 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,043,620B, BPFP=0.6624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,050,708B, BPFP=0.6669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 601,812B, BPFP=0.3820 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 611,160B, BPFP=0.3879 +⌛️ [2/4] FRONTEND: Frontend time: 33.220s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.752s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14577075 4.43264448 + layer.9.1 0.14541274 4.42268208 + layer.19.0 0.13069581 7.66839276 + layer.19.1 0.13545482 7.61872931 + layer.29.0 0.11331055 61.45113950 + layer.29.1 0.11244963 58.39728226 + layer.39.0 32.27446072 2727.63048424 + layer.39.1 16.59366367 2730.13714657 + ------------------------------------------------------------------------------------- + TOTAL 6.20640234 700.21981265 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4985604 +BPFP 0.3956 bits/point +EBPFP 0.3956 equivalent bits/point +MSE 700.219813 +---------------------- ---------------------------------------------------------- +Time: 66.181s Load: 1.209s, Pack+Encode: 33.220s, Decode+Unpack: 31.752s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 700.2198 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.202s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,576B, BPFP=0.1134 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 181,460B, BPFP=0.1152 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 513,412B, BPFP=0.3259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 509,996B, BPFP=0.3237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 958,192B, BPFP=0.6082 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 963,932B, BPFP=0.6119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 556,960B, BPFP=0.3535 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 556,964B, BPFP=0.3535 +⌛️ [2/4] FRONTEND: Frontend time: 33.193s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.298s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585920 4.42649534 + layer.9.1 0.14576220 4.42517126 + layer.19.0 0.12270736 7.19318736 + layer.19.1 0.12453605 7.23490946 + layer.29.0 0.11393550 50.85832893 + layer.29.1 0.11678154 54.26832142 + layer.39.0 53.83016636 2390.55134872 + layer.39.1 40.65720720 2468.49171271 + ------------------------------------------------------------------------------------- + TOTAL 11.90711942 623.43118440 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4419492 +BPFP 0.3507 bits/point +EBPFP 0.3507 equivalent bits/point +MSE 623.431184 +---------------------- ---------------------------------------------------------- +Time: 65.693s Load: 1.202s, Pack+Encode: 33.193s, Decode+Unpack: 31.298s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 623.4312 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,880B, BPFP=0.1224 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 191,496B, BPFP=0.1216 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 586,360B, BPFP=0.3722 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 583,448B, BPFP=0.3703 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,144,796B, BPFP=0.7267 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,135,388B, BPFP=0.7207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,392B, BPFP=0.3411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 535,800B, BPFP=0.3401 +⌛️ [2/4] FRONTEND: Frontend time: 33.220s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.383s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568599 4.40695711 + layer.9.1 0.03329684 4.40950341 + layer.19.0 0.11848472 7.45352172 + layer.19.1 0.11973745 7.53672408 + layer.29.0 0.10886538 72.60990819 + layer.29.1 0.10946879 80.71855196 + layer.39.0 14.08931437 2214.65030874 + layer.39.1 9.95616799 2248.23659409 + ------------------------------------------------------------------------------------- + TOTAL 3.08512769 580.00275866 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4907560 +BPFP 0.3894 bits/point +EBPFP 0.3894 equivalent bits/point +MSE 580.002759 +---------------------- ---------------------------------------------------------- +Time: 65.810s Load: 1.206s, Pack+Encode: 33.220s, Decode+Unpack: 31.383s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 580.0028 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.194s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,960B, BPFP=0.1180 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,492B, BPFP=0.1165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 539,848B, BPFP=0.3427 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 545,068B, BPFP=0.3460 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,026,128B, BPFP=0.6513 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,040,816B, BPFP=0.6607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 538,068B, BPFP=0.3415 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 538,108B, BPFP=0.3416 +⌛️ [2/4] FRONTEND: Frontend time: 33.264s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.559s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14493624 4.35376952 + layer.9.1 0.14482686 4.36799173 + layer.19.0 0.11946148 7.48362473 + layer.19.1 0.12828579 7.28465416 + layer.29.0 0.10467725 60.16171393 + layer.29.1 0.10613328 61.93052283 + layer.39.0 22.00188902 2578.62040949 + layer.39.1 19.26198661 2661.68540786 + ------------------------------------------------------------------------------------- + TOTAL 5.25152457 673.23601178 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4597488 +BPFP 0.3648 bits/point +EBPFP 0.3648 equivalent bits/point +MSE 673.236012 +---------------------- ---------------------------------------------------------- +Time: 66.017s Load: 1.194s, Pack+Encode: 33.264s, Decode+Unpack: 31.559s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 673.2360 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,224B, BPFP=0.1163 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 185,700B, BPFP=0.1179 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 553,384B, BPFP=0.3513 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 547,788B, BPFP=0.3477 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,038,388B, BPFP=0.6591 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,017,540B, BPFP=0.6459 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 560,360B, BPFP=0.3557 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 558,656B, BPFP=0.3546 +⌛️ [2/4] FRONTEND: Frontend time: 32.222s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.175s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14475438 4.37341186 + layer.9.1 0.14492096 4.37750060 + layer.19.0 0.11744098 7.76800658 + layer.19.1 0.11578254 7.68215287 + layer.29.0 0.11402616 64.40701170 + layer.29.1 0.11062706 58.46989255 + layer.39.0 28.92800668 3106.98570036 + layer.39.1 10.80449708 3022.01689958 + ------------------------------------------------------------------------------------- + TOTAL 5.06000698 784.51007201 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4645040 +BPFP 0.3686 bits/point +EBPFP 0.3686 equivalent bits/point +MSE 784.510072 +---------------------- ---------------------------------------------------------- +Time: 64.605s Load: 1.208s, Pack+Encode: 32.222s, Decode+Unpack: 31.175s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 784.5101 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 169,416B, BPFP=0.1075 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,704B, BPFP=0.1109 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 455,964B, BPFP=0.2894 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 458,292B, BPFP=0.2909 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 770,560B, BPFP=0.4891 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 767,072B, BPFP=0.4869 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,360B, BPFP=0.2903 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 464,916B, BPFP=0.2951 +⌛️ [2/4] FRONTEND: Frontend time: 33.116s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.389s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14546214 4.35340518 + layer.9.1 0.14553630 4.37702390 + layer.19.0 0.04765745 6.73641509 + layer.19.1 0.04191649 6.76166099 + layer.29.0 0.16505912 49.96344857 + layer.29.1 0.15755973 44.87741713 + layer.39.0 42.51041751 2197.56581085 + layer.39.1 31.38856333 2181.16265843 + ------------------------------------------------------------------------------------- + TOTAL 9.32527151 561.97473002 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3718284 +BPFP 0.2950 bits/point +EBPFP 0.2950 equivalent bits/point +MSE 561.974730 +---------------------- ---------------------------------------------------------- +Time: 65.584s Load: 1.080s, Pack+Encode: 33.116s, Decode+Unpack: 31.389s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 561.9747 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.220s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,760B, BPFP=0.1192 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,560B, BPFP=0.1165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 501,888B, BPFP=0.3186 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 500,068B, BPFP=0.3174 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 924,292B, BPFP=0.5867 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 914,432B, BPFP=0.5804 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,440B, BPFP=0.3265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 511,656B, BPFP=0.3248 +⌛️ [2/4] FRONTEND: Frontend time: 33.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.641s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03343166 4.42103840 + layer.9.1 0.03311388 4.39868918 + layer.19.0 0.03842411 7.15258178 + layer.19.1 0.03806642 7.10292150 + layer.29.0 4.26870163 44.01728551 + layer.29.1 4.26552788 50.66451190 + layer.39.0 33.95300821 1936.98342541 + layer.39.1 48.19954501 1810.44361391 + ------------------------------------------------------------------------------------- + TOTAL 11.35372735 483.14800845 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4238096 +BPFP 0.3363 bits/point +EBPFP 0.3363 equivalent bits/point +MSE 483.148008 +---------------------- ---------------------------------------------------------- +Time: 65.986s Load: 1.220s, Pack+Encode: 33.125s, Decode+Unpack: 31.641s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 483.1480 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.199s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,748B, BPFP=0.1262 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 198,644B, BPFP=0.1261 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 509,464B, BPFP=0.3234 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 497,740B, BPFP=0.3159 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 851,836B, BPFP=0.5407 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 858,888B, BPFP=0.5452 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 507,920B, BPFP=0.3224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 518,716B, BPFP=0.3293 +⌛️ [2/4] FRONTEND: Frontend time: 33.013s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.626s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549638 4.37442333 + layer.9.1 0.14520178 4.36854809 + layer.19.0 0.11487435 7.34143316 + layer.19.1 0.11481158 7.32612808 + layer.29.0 0.10827909 51.96143768 + layer.29.1 0.10618535 45.22750447 + layer.39.0 9.83978281 2108.65550861 + layer.39.1 9.67554703 2099.34969126 + ------------------------------------------------------------------------------------- + TOTAL 2.53127230 541.07558433 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4141956 +BPFP 0.3286 bits/point +EBPFP 0.3286 equivalent bits/point +MSE 541.075584 +---------------------- ---------------------------------------------------------- +Time: 65.837s Load: 1.199s, Pack+Encode: 33.013s, Decode+Unpack: 31.626s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 541.0756 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.214s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,732B, BPFP=0.1090 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 166,504B, BPFP=0.1057 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 478,924B, BPFP=0.3040 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 473,888B, BPFP=0.3008 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 917,440B, BPFP=0.5823 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 903,720B, BPFP=0.5736 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 481,144B, BPFP=0.3054 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 477,604B, BPFP=0.3032 +⌛️ [2/4] FRONTEND: Frontend time: 33.227s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.626s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092318 4.41514983 + layer.9.1 0.00095285 4.42762742 + layer.19.0 0.08568402 7.21085562 + layer.19.1 0.08404610 7.22391305 + layer.29.0 0.12100375 53.15790035 + layer.29.1 0.12795564 50.48576129 + layer.39.0 12.85620633 2830.48293793 + layer.39.1 12.98640239 2933.01787455 + ------------------------------------------------------------------------------------- + TOTAL 3.28289678 736.30275251 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4070956 +BPFP 0.3230 bits/point +EBPFP 0.3230 equivalent bits/point +MSE 736.302753 +---------------------- ---------------------------------------------------------- +Time: 66.068s Load: 1.214s, Pack+Encode: 33.227s, Decode+Unpack: 31.626s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 736.3028 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.207s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,712B, BPFP=0.1058 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 166,536B, BPFP=0.1057 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 468,460B, BPFP=0.2974 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 470,652B, BPFP=0.2987 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 894,840B, BPFP=0.5680 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 926,020B, BPFP=0.5878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,696B, BPFP=0.3140 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 497,872B, BPFP=0.3160 +⌛️ [2/4] FRONTEND: Frontend time: 33.074s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.417s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00113376 4.42426642 + layer.9.1 0.00100095 4.44309786 + layer.19.0 0.00983371 7.17141481 + layer.19.1 0.00806405 7.31716414 + layer.29.0 4.28365570 52.02380565 + layer.29.1 4.28597952 52.06229180 + layer.39.0 8.41906814 2089.24390640 + layer.39.1 8.59662605 2132.63357166 + ------------------------------------------------------------------------------------- + TOTAL 3.20067024 543.66493984 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4085788 +BPFP 0.3242 bits/point +EBPFP 0.3242 equivalent bits/point +MSE 543.664940 +---------------------- ---------------------------------------------------------- +Time: 65.699s Load: 1.207s, Pack+Encode: 33.074s, Decode+Unpack: 31.417s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 543.6649 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,448B, BPFP=0.1171 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,680B, BPFP=0.1172 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 525,456B, BPFP=0.3335 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 521,480B, BPFP=0.3310 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 949,060B, BPFP=0.6024 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 959,116B, BPFP=0.6088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,132B, BPFP=0.3143 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 489,432B, BPFP=0.3107 +⌛️ [2/4] FRONTEND: Frontend time: 33.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.688s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550294 4.41361786 + layer.9.1 0.14526658 4.40370180 + layer.19.0 0.11599200 7.69681368 + layer.19.1 0.11361485 7.57526228 + layer.29.0 4.26439454 62.60608446 + layer.29.1 4.25587461 62.65123294 + layer.39.0 8.37236706 1853.76633084 + layer.39.1 8.35116642 1854.95336367 + ------------------------------------------------------------------------------------- + TOTAL 3.22052237 482.25830094 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4308804 +BPFP 0.3419 bits/point +EBPFP 0.3419 equivalent bits/point +MSE 482.258301 +---------------------- ---------------------------------------------------------- +Time: 66.203s Load: 1.223s, Pack+Encode: 33.292s, Decode+Unpack: 31.688s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 482.2583 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.199s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,808B, BPFP=0.1084 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,408B, BPFP=0.1107 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 538,308B, BPFP=0.3417 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 540,464B, BPFP=0.3431 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,011,124B, BPFP=0.6418 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,031,224B, BPFP=0.6546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 508,940B, BPFP=0.3230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 521,940B, BPFP=0.3313 +⌛️ [2/4] FRONTEND: Frontend time: 33.251s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00080404 4.46220573 + layer.9.1 0.00082438 4.44390748 + layer.19.0 0.00843097 7.13883691 + layer.19.1 0.00674472 7.23827173 + layer.29.0 4.27713270 59.74659266 + layer.29.1 4.27133426 59.42586123 + layer.39.0 22.97048921 1994.11309717 + layer.39.1 18.06488920 2030.69824504 + ------------------------------------------------------------------------------------- + TOTAL 6.20008118 520.90837724 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4497216 +BPFP 0.3568 bits/point +EBPFP 0.3568 equivalent bits/point +MSE 520.908377 +---------------------- ---------------------------------------------------------- +Time: 65.981s Load: 1.199s, Pack+Encode: 33.251s, Decode+Unpack: 31.531s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 520.9084 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,580B, BPFP=0.1057 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,200B, BPFP=0.1087 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 511,216B, BPFP=0.3245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 521,012B, BPFP=0.3307 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 998,380B, BPFP=0.6337 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,005,920B, BPFP=0.6385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 520,104B, BPFP=0.3301 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 522,136B, BPFP=0.3314 +⌛️ [2/4] FRONTEND: Frontend time: 33.027s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14506060 4.35826038 + layer.9.1 0.14523201 4.36891878 + layer.19.0 0.04621643 7.15409883 + layer.19.1 0.04629335 7.16043046 + layer.29.0 4.27940669 57.48700032 + layer.29.1 4.27759670 59.83304050 + layer.39.0 19.91382637 2095.08027299 + layer.39.1 24.01088215 2078.01007475 + ------------------------------------------------------------------------------------- + TOTAL 6.60806429 539.18151213 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4416548 +BPFP 0.3504 bits/point +EBPFP 0.3504 equivalent bits/point +MSE 539.181512 +---------------------- ---------------------------------------------------------- +Time: 65.847s Load: 1.215s, Pack+Encode: 33.027s, Decode+Unpack: 31.605s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 539.1815 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,332B, BPFP=0.1088 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,968B, BPFP=0.1092 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 399,320B, BPFP=0.2535 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 395,064B, BPFP=0.2508 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 655,160B, BPFP=0.4159 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 658,488B, BPFP=0.4180 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,136B, BPFP=0.2718 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 428,812B, BPFP=0.2722 +⌛️ [2/4] FRONTEND: Frontend time: 33.118s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.585s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66985745 4.44789625 + layer.9.1 2.66884121 4.44897564 + layer.19.0 3.21935619 6.47711664 + layer.19.1 3.21606501 6.42843133 + layer.29.0 4.24164606 36.77408241 + layer.29.1 4.23648681 38.85304528 + layer.39.0 8.06392628 1562.88040299 + layer.39.1 8.17747540 1666.50324992 + ------------------------------------------------------------------------------------- + TOTAL 4.56170680 415.85165006 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3308280 +BPFP 0.2625 bits/point +EBPFP 0.2625 equivalent bits/point +MSE 415.851650 +---------------------- ---------------------------------------------------------- +Time: 65.910s Load: 1.206s, Pack+Encode: 33.118s, Decode+Unpack: 31.585s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 415.8517 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,508B, BPFP=0.1032 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 162,672B, BPFP=0.1033 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 411,700B, BPFP=0.2613 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 414,212B, BPFP=0.2629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 739,056B, BPFP=0.4691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 745,848B, BPFP=0.4734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,740B, BPFP=0.2937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 460,752B, BPFP=0.2925 +⌛️ [2/4] FRONTEND: Frontend time: 33.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.353s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66795352 4.46216891 + layer.9.1 2.66862889 4.43697316 + layer.19.0 3.22250645 6.73625132 + layer.19.1 3.22577319 7.02584638 + layer.29.0 4.25792136 45.74758287 + layer.29.1 4.25014663 43.98270942 + layer.39.0 8.65209937 1961.73220669 + layer.39.1 8.58450170 1806.10545986 + ------------------------------------------------------------------------------------- + TOTAL 4.69119139 485.02864983 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3559488 +BPFP 0.2824 bits/point +EBPFP 0.2824 equivalent bits/point +MSE 485.028650 +---------------------- ---------------------------------------------------------- +Time: 65.690s Load: 1.206s, Pack+Encode: 33.130s, Decode+Unpack: 31.353s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 485.0286 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.204s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,492B, BPFP=0.1101 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 172,696B, BPFP=0.1096 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 513,412B, BPFP=0.3259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 512,980B, BPFP=0.3256 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 992,600B, BPFP=0.6301 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 983,552B, BPFP=0.6243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,976B, BPFP=0.3047 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 474,276B, BPFP=0.3010 +⌛️ [2/4] FRONTEND: Frontend time: 33.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.645s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092775 4.46290269 + layer.9.1 0.00093166 4.48499928 + layer.19.0 0.08227225 7.35819183 + layer.19.1 0.08381199 7.18209193 + layer.29.0 0.10725604 51.83621425 + layer.29.1 0.10756977 50.41296819 + layer.39.0 7.96294394 1791.07881053 + layer.39.1 7.95922050 1763.21823204 + ------------------------------------------------------------------------------------- + TOTAL 2.03811674 460.00430134 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4302984 +BPFP 0.3414 bits/point +EBPFP 0.3414 equivalent bits/point +MSE 460.004301 +---------------------- ---------------------------------------------------------- +Time: 66.134s Load: 1.204s, Pack+Encode: 33.285s, Decode+Unpack: 31.645s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 460.0043 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,584B, BPFP=0.1057 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 163,848B, BPFP=0.1040 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 436,356B, BPFP=0.2770 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 426,544B, BPFP=0.2707 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 771,364B, BPFP=0.4896 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 755,600B, BPFP=0.4796 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,388B, BPFP=0.2916 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 463,164B, BPFP=0.2940 +⌛️ [2/4] FRONTEND: Frontend time: 33.317s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.433s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66308118 4.48180585 + layer.9.1 2.66351027 4.48877001 + layer.19.0 3.21594155 7.38445206 + layer.19.1 3.21498593 7.41804873 + layer.29.0 4.33566519 46.82645434 + layer.29.1 4.34101296 42.79583909 + layer.39.0 8.65310735 1926.05882353 + layer.39.1 8.66575030 1755.38625284 + ------------------------------------------------------------------------------------- + TOTAL 4.71913184 474.35505581 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3642848 +BPFP 0.2890 bits/point +EBPFP 0.2890 equivalent bits/point +MSE 474.355056 +---------------------- ---------------------------------------------------------- +Time: 65.958s Load: 1.208s, Pack+Encode: 33.317s, Decode+Unpack: 31.433s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 474.3551 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.220s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,644B, BPFP=0.1090 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 176,632B, BPFP=0.1121 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 463,560B, BPFP=0.2942 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 458,464B, BPFP=0.2910 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 776,316B, BPFP=0.4928 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 782,904B, BPFP=0.4969 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 451,744B, BPFP=0.2867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 443,592B, BPFP=0.2816 +⌛️ [2/4] FRONTEND: Frontend time: 33.200s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.65959259 4.48856149 + layer.9.1 2.65993726 4.49212307 + layer.19.0 3.20866700 7.72520604 + layer.19.1 3.21007805 7.32927390 + layer.29.0 4.27255361 46.29341181 + layer.29.1 4.27602442 47.22003981 + layer.39.0 19.11658068 1731.92411440 + layer.39.1 9.60360322 1653.38024049 + ------------------------------------------------------------------------------------- + TOTAL 6.12587960 437.85662138 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3724856 +BPFP 0.2955 bits/point +EBPFP 0.2955 equivalent bits/point +MSE 437.856621 +---------------------- ---------------------------------------------------------- +Time: 66.010s Load: 1.220s, Pack+Encode: 33.200s, Decode+Unpack: 31.590s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 437.8566 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,164B, BPFP=0.1156 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 179,632B, BPFP=0.1140 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 466,860B, BPFP=0.2963 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 461,756B, BPFP=0.2931 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 844,696B, BPFP=0.5362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 820,328B, BPFP=0.5207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,060B, BPFP=0.2977 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 463,684B, BPFP=0.2943 +⌛️ [2/4] FRONTEND: Frontend time: 32.747s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.171s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67078767 4.44185375 + layer.9.1 2.67131261 4.44460793 + layer.19.0 3.30595795 7.29111211 + layer.19.1 3.30543206 7.47366423 + layer.29.0 0.11228124 52.29126889 + layer.29.1 0.11507649 51.74844613 + layer.39.0 11.41791162 1896.01218720 + layer.39.1 11.38150745 1970.40250244 + ------------------------------------------------------------------------------------- + TOTAL 4.37253339 499.26320533 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3888180 +BPFP 0.3085 bits/point +EBPFP 0.3085 equivalent bits/point +MSE 499.263205 +---------------------- ---------------------------------------------------------- +Time: 65.122s Load: 1.203s, Pack+Encode: 32.747s, Decode+Unpack: 31.171s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 499.2632 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.212s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,036B, BPFP=0.1181 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,308B, BPFP=0.1164 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 498,824B, BPFP=0.3166 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 497,332B, BPFP=0.3157 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 961,732B, BPFP=0.6105 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 923,848B, BPFP=0.5864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,940B, BPFP=0.3205 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 507,988B, BPFP=0.3224 +⌛️ [2/4] FRONTEND: Frontend time: 33.022s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.329s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14463971 4.35905128 + layer.9.1 0.14470460 4.34829163 + layer.19.0 0.12255537 7.19659977 + layer.19.1 0.11825690 7.02045608 + layer.29.0 0.11949990 56.42409409 + layer.29.1 0.11467140 63.25036054 + layer.39.0 10.68243977 1991.56873578 + layer.39.1 10.40156301 2065.74228144 + ------------------------------------------------------------------------------------- + TOTAL 2.73104133 524.98873383 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4264008 +BPFP 0.3383 bits/point +EBPFP 0.3383 equivalent bits/point +MSE 524.988734 +---------------------- ---------------------------------------------------------- +Time: 65.564s Load: 1.212s, Pack+Encode: 33.022s, Decode+Unpack: 31.329s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 524.9887 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.168s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,028B, BPFP=0.1181 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,008B, BPFP=0.1162 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 508,428B, BPFP=0.3227 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 498,780B, BPFP=0.3166 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 941,084B, BPFP=0.5974 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 906,852B, BPFP=0.5756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 518,268B, BPFP=0.3290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 512,092B, BPFP=0.3251 +⌛️ [2/4] FRONTEND: Frontend time: 33.219s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.652s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14485273 4.32354211 + layer.9.1 0.14484227 4.35743774 + layer.19.0 0.11969613 7.50857864 + layer.19.1 0.11916645 7.53363539 + layer.29.0 0.11480527 56.60111310 + layer.29.1 0.11451660 61.93329034 + layer.39.0 11.00270276 2296.03428664 + layer.39.1 11.01557422 2164.42915177 + ------------------------------------------------------------------------------------- + TOTAL 2.84701956 575.34012947 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4254540 +BPFP 0.3376 bits/point +EBPFP 0.3376 equivalent bits/point +MSE 575.340129 +---------------------- ---------------------------------------------------------- +Time: 66.039s Load: 1.168s, Pack+Encode: 33.219s, Decode+Unpack: 31.652s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 575.3401 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.207s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,612B, BPFP=0.1121 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,816B, BPFP=0.1110 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 447,320B, BPFP=0.2839 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 446,084B, BPFP=0.2832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 767,348B, BPFP=0.4871 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 778,680B, BPFP=0.4943 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,900B, BPFP=0.3135 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 487,276B, BPFP=0.3093 +⌛️ [2/4] FRONTEND: Frontend time: 32.971s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14481488 4.35780907 + layer.9.1 0.14470567 4.34987343 + layer.19.0 0.03819180 7.13979602 + layer.19.1 0.04002141 6.97448243 + layer.29.0 0.11241068 45.50496628 + layer.29.1 0.11133552 48.23733547 + layer.39.0 31.78807483 2371.65404615 + layer.39.1 43.50691623 2292.25690608 + ------------------------------------------------------------------------------------- + TOTAL 9.48580888 597.55940187 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3772036 +BPFP 0.2993 bits/point +EBPFP 0.2993 equivalent bits/point +MSE 597.559402 +---------------------- ---------------------------------------------------------- +Time: 65.767s Load: 1.207s, Pack+Encode: 32.971s, Decode+Unpack: 31.590s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 597.5594 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.212s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,928B, BPFP=0.1167 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,992B, BPFP=0.1168 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 495,748B, BPFP=0.3147 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 485,716B, BPFP=0.3083 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 939,016B, BPFP=0.5960 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 920,384B, BPFP=0.5842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 517,776B, BPFP=0.3287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 524,064B, BPFP=0.3326 +⌛️ [2/4] FRONTEND: Frontend time: 33.096s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14528272 4.38689775 + layer.9.1 0.14516892 4.29360255 + layer.19.0 0.11319376 7.10298370 + layer.19.1 0.11666145 6.85734761 + layer.29.0 0.21118872 60.28362650 + layer.29.1 0.20646930 61.61468151 + layer.39.0 14.37750853 2704.58173546 + layer.39.1 21.76644002 2747.58206045 + ------------------------------------------------------------------------------------- + TOTAL 4.63523918 699.58786694 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4250624 +BPFP 0.3373 bits/point +EBPFP 0.3373 equivalent bits/point +MSE 699.587867 +---------------------- ---------------------------------------------------------- +Time: 65.793s Load: 1.212s, Pack+Encode: 33.096s, Decode+Unpack: 31.485s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 699.5879 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.207s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,520B, BPFP=0.1114 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,264B, BPFP=0.1112 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 482,856B, BPFP=0.3065 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 480,124B, BPFP=0.3048 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 800,620B, BPFP=0.5082 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 796,696B, BPFP=0.5057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,836B, BPFP=0.3166 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 493,020B, BPFP=0.3129 +⌛️ [2/4] FRONTEND: Frontend time: 32.663s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14454765 4.33891702 + layer.9.1 0.14475082 4.34856680 + layer.19.0 0.04087094 7.44956532 + layer.19.1 0.11687931 6.92286648 + layer.29.0 0.10817139 47.12434494 + layer.29.1 0.10802081 46.75831776 + layer.39.0 19.80422286 2009.16509587 + layer.39.1 34.29222355 2000.33977901 + ------------------------------------------------------------------------------------- + TOTAL 6.84496092 515.80593165 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3902936 +BPFP 0.3097 bits/point +EBPFP 0.3097 equivalent bits/point +MSE 515.805932 +---------------------- ---------------------------------------------------------- +Time: 65.470s Load: 1.207s, Pack+Encode: 32.663s, Decode+Unpack: 31.600s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 515.8059 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.219s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,352B, BPFP=0.1088 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 170,252B, BPFP=0.1081 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 459,104B, BPFP=0.2914 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 457,660B, BPFP=0.2905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 733,544B, BPFP=0.4656 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 737,740B, BPFP=0.4683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,648B, BPFP=0.2981 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 469,720B, BPFP=0.2982 +⌛️ [2/4] FRONTEND: Frontend time: 33.023s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.778s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14462205 4.33742853 + layer.9.1 0.14495783 4.36440254 + layer.19.0 0.04322015 5.82402007 + layer.19.1 0.03788725 7.83426938 + layer.29.0 0.10021623 43.80465957 + layer.29.1 0.10137775 45.98287699 + layer.39.0 58.66958482 1807.12154696 + layer.39.1 72.48303949 1845.41306467 + ------------------------------------------------------------------------------------- + TOTAL 16.46561320 470.58528359 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3669020 +BPFP 0.2911 bits/point +EBPFP 0.2911 equivalent bits/point +MSE 470.585284 +---------------------- ---------------------------------------------------------- +Time: 66.021s Load: 1.219s, Pack+Encode: 33.023s, Decode+Unpack: 31.778s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 470.5853 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,668B, BPFP=0.1185 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 189,232B, BPFP=0.1201 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 519,612B, BPFP=0.3298 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 529,320B, BPFP=0.3360 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,017,232B, BPFP=0.6457 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,029,780B, BPFP=0.6537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,364B, BPFP=0.3163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 502,460B, BPFP=0.3189 +⌛️ [2/4] FRONTEND: Frontend time: 33.054s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.625s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527927 4.38674795 + layer.9.1 0.14528875 4.36816533 + layer.19.0 0.12591341 21.58685154 + layer.19.1 0.13556211 7.59746583 + layer.29.0 0.11238900 78.04001970 + layer.29.1 0.11028371 74.99310916 + layer.39.0 11.48751193 2213.81670458 + layer.39.1 11.29491489 2179.07491063 + ------------------------------------------------------------------------------------- + TOTAL 2.94464289 572.98299684 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4472668 +BPFP 0.3549 bits/point +EBPFP 0.3549 equivalent bits/point +MSE 572.982997 +---------------------- ---------------------------------------------------------- +Time: 65.887s Load: 1.208s, Pack+Encode: 33.054s, Decode+Unpack: 31.625s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.9830 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.204s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,596B, BPFP=0.1146 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 181,516B, BPFP=0.1152 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 494,276B, BPFP=0.3137 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 495,576B, BPFP=0.3146 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 915,848B, BPFP=0.5813 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 919,996B, BPFP=0.5840 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 489,424B, BPFP=0.3107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 481,148B, BPFP=0.3054 +⌛️ [2/4] FRONTEND: Frontend time: 33.079s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14489184 4.31502694 + layer.9.1 0.14511764 4.39157459 + layer.19.0 0.03976490 7.61507061 + layer.19.1 0.11370806 7.41791987 + layer.29.0 0.10933599 67.38456695 + layer.29.1 0.11012027 56.01967216 + layer.39.0 9.10787636 1738.58969776 + layer.39.1 9.00026152 1731.71368216 + ------------------------------------------------------------------------------------- + TOTAL 2.34638457 452.18090138 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4158380 +BPFP 0.3299 bits/point +EBPFP 0.3299 equivalent bits/point +MSE 452.180901 +---------------------- ---------------------------------------------------------- +Time: 65.748s Load: 1.204s, Pack+Encode: 33.079s, Decode+Unpack: 31.465s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 452.1809 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.193s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,288B, BPFP=0.1132 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 180,292B, BPFP=0.1144 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 458,264B, BPFP=0.2909 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 452,340B, BPFP=0.2871 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 797,168B, BPFP=0.5060 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 798,596B, BPFP=0.5069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,000B, BPFP=0.2641 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 409,640B, BPFP=0.2600 +⌛️ [2/4] FRONTEND: Frontend time: 33.045s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.210s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083307 4.41230012 + layer.9.1 0.00247171 4.40240722 + layer.19.0 0.00642632 7.75056810 + layer.19.1 0.00641681 7.76876447 + layer.29.0 0.10256791 58.77544077 + layer.29.1 0.10162673 55.26857532 + layer.39.0 8.50517638 1635.15550861 + layer.39.1 8.55767781 1544.42118947 + ------------------------------------------------------------------------------------- + TOTAL 2.16039959 414.74434426 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3690588 +BPFP 0.2928 bits/point +EBPFP 0.2928 equivalent bits/point +MSE 414.744344 +---------------------- ---------------------------------------------------------- +Time: 65.448s Load: 1.193s, Pack+Encode: 33.045s, Decode+Unpack: 31.210s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 414.7443 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.195s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,832B, BPFP=0.1110 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,600B, BPFP=0.1134 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 464,096B, BPFP=0.2946 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 467,740B, BPFP=0.2969 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 920,488B, BPFP=0.5843 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 920,436B, BPFP=0.5842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 503,968B, BPFP=0.3199 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 505,460B, BPFP=0.3208 +⌛️ [2/4] FRONTEND: Frontend time: 32.460s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.206s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00064391 4.31168879 + layer.9.1 0.00065402 4.41125341 + layer.19.0 0.08134466 7.18036224 + layer.19.1 0.08141702 7.01273371 + layer.29.0 0.11551180 65.90783434 + layer.29.1 0.11251285 62.35856760 + layer.39.0 10.61319619 2173.42996425 + layer.39.1 10.43102047 2217.47968801 + ------------------------------------------------------------------------------------- + TOTAL 2.67953762 567.76151154 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4135620 +BPFP 0.3281 bits/point +EBPFP 0.3281 equivalent bits/point +MSE 567.761512 +---------------------- ---------------------------------------------------------- +Time: 64.861s Load: 1.195s, Pack+Encode: 32.460s, Decode+Unpack: 31.206s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 567.7615 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.218s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,916B, BPFP=0.1117 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,204B, BPFP=0.1131 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 509,732B, BPFP=0.3236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 502,908B, BPFP=0.3192 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 947,804B, BPFP=0.6016 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 933,536B, BPFP=0.5926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 551,184B, BPFP=0.3499 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 551,192B, BPFP=0.3499 +⌛️ [2/4] FRONTEND: Frontend time: 33.168s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.642s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14441776 4.30377759 + layer.9.1 0.14449203 4.34354307 + layer.19.0 0.11315974 7.46758650 + layer.19.1 0.11435745 7.52606409 + layer.29.0 0.12811458 50.35466770 + layer.29.1 0.12952277 49.47584396 + layer.39.0 31.10682331 2592.76356841 + layer.39.1 16.99297713 2413.42801430 + ------------------------------------------------------------------------------------- + TOTAL 6.10923309 641.20788320 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4350476 +BPFP 0.3452 bits/point +EBPFP 0.3452 equivalent bits/point +MSE 641.207883 +---------------------- ---------------------------------------------------------- +Time: 66.028s Load: 1.218s, Pack+Encode: 33.168s, Decode+Unpack: 31.642s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 641.2079 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,088B, BPFP=0.1054 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,680B, BPFP=0.1090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 476,952B, BPFP=0.3027 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 475,196B, BPFP=0.3016 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 891,216B, BPFP=0.5657 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 873,696B, BPFP=0.5546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,928B, BPFP=0.3065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 488,940B, BPFP=0.3104 +⌛️ [2/4] FRONTEND: Frontend time: 32.976s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078516 4.45202879 + layer.9.1 0.00079184 4.44426929 + layer.19.0 3.22632161 7.90574918 + layer.19.1 3.22513146 7.79292051 + layer.29.0 0.10494786 46.66597944 + layer.29.1 0.10251782 42.97287841 + layer.39.0 10.88842496 2016.86139097 + layer.39.1 10.78217420 2042.40705232 + ------------------------------------------------------------------------------------- + TOTAL 3.54138686 521.68778361 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4026696 +BPFP 0.3195 bits/point +EBPFP 0.3195 equivalent bits/point +MSE 521.687784 +---------------------- ---------------------------------------------------------- +Time: 65.628s Load: 1.208s, Pack+Encode: 32.976s, Decode+Unpack: 31.444s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 521.6878 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.204s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,964B, BPFP=0.1168 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 182,244B, BPFP=0.1157 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 472,564B, BPFP=0.3000 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 469,760B, BPFP=0.2982 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 870,116B, BPFP=0.5523 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 860,348B, BPFP=0.5461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 480,244B, BPFP=0.3048 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 483,716B, BPFP=0.3070 +⌛️ [2/4] FRONTEND: Frontend time: 33.081s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.720s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561636 4.41917255 + layer.9.1 0.14552785 4.40023099 + layer.19.0 0.04069186 6.96530839 + layer.19.1 0.03840616 7.01759590 + layer.29.0 0.11346353 57.06876117 + layer.29.1 0.11182956 56.76885461 + layer.39.0 10.19697364 1812.32954176 + layer.39.1 10.11578978 1873.80435489 + ------------------------------------------------------------------------------------- + TOTAL 2.61353734 477.84672753 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4002956 +BPFP 0.3176 bits/point +EBPFP 0.3176 equivalent bits/point +MSE 477.846728 +---------------------- ---------------------------------------------------------- +Time: 66.006s Load: 1.204s, Pack+Encode: 33.081s, Decode+Unpack: 31.720s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 477.8467 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,272B, BPFP=0.1119 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 168,872B, BPFP=0.1072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 489,760B, BPFP=0.3109 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 487,728B, BPFP=0.3096 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 930,256B, BPFP=0.5905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 928,404B, BPFP=0.5893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 500,456B, BPFP=0.3177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 494,180B, BPFP=0.3137 +⌛️ [2/4] FRONTEND: Frontend time: 33.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.367s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14399445 4.41951659 + layer.9.1 0.14558028 4.40749221 + layer.19.0 0.03837104 7.25791977 + layer.19.1 0.04376782 7.30410290 + layer.29.0 0.11695251 68.15669686 + layer.29.1 0.13128335 56.15175597 + layer.39.0 11.28613757 2201.18589535 + layer.39.1 11.84408769 2188.16932077 + ------------------------------------------------------------------------------------- + TOTAL 2.96877184 567.13158755 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4175928 +BPFP 0.3313 bits/point +EBPFP 0.3313 equivalent bits/point +MSE 567.131588 +---------------------- ---------------------------------------------------------- +Time: 65.712s Load: 1.206s, Pack+Encode: 33.139s, Decode+Unpack: 31.367s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 567.1316 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,892B, BPFP=0.1110 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,336B, BPFP=0.1088 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 529,940B, BPFP=0.3364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 525,968B, BPFP=0.3339 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,042,756B, BPFP=0.6619 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,048,896B, BPFP=0.6658 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,480B, BPFP=0.3570 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 575,276B, BPFP=0.3652 +⌛️ [2/4] FRONTEND: Frontend time: 33.233s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.316s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14502686 4.37417102 + layer.9.1 0.03259508 4.36102281 + layer.19.0 0.11326540 7.18726260 + layer.19.1 0.11324834 7.09615570 + layer.29.0 0.12250664 58.08087220 + layer.29.1 0.12058897 61.19650837 + layer.39.0 16.17915050 2392.70555736 + layer.39.1 21.66230805 2529.13292168 + ------------------------------------------------------------------------------------- + TOTAL 4.81108623 633.01680897 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4631544 +BPFP 0.3675 bits/point +EBPFP 0.3675 equivalent bits/point +MSE 633.016809 +---------------------- ---------------------------------------------------------- +Time: 65.757s Load: 1.208s, Pack+Encode: 33.233s, Decode+Unpack: 31.316s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 633.0168 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.163s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,760B, BPFP=0.1033 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 162,076B, BPFP=0.1029 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 427,040B, BPFP=0.2711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 428,804B, BPFP=0.2722 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 768,332B, BPFP=0.4877 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 772,096B, BPFP=0.4901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 439,748B, BPFP=0.2791 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 438,648B, BPFP=0.2784 +⌛️ [2/4] FRONTEND: Frontend time: 33.253s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66733019 4.46682195 + layer.9.1 2.66763138 4.47228207 + layer.19.0 3.22293078 7.15849257 + layer.19.1 3.22376992 7.10263015 + layer.29.0 4.27658332 45.20744130 + layer.29.1 4.27160529 45.91921413 + layer.39.0 7.81683598 1738.68329542 + layer.39.1 9.86231960 1767.38024049 + ------------------------------------------------------------------------------------- + TOTAL 4.75112581 452.54880226 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3599504 +BPFP 0.2856 bits/point +EBPFP 0.2856 equivalent bits/point +MSE 452.548802 +---------------------- ---------------------------------------------------------- +Time: 65.893s Load: 1.163s, Pack+Encode: 33.253s, Decode+Unpack: 31.477s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 452.5488 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,776B, BPFP=0.1084 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,020B, BPFP=0.1086 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 398,616B, BPFP=0.2530 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 404,648B, BPFP=0.2569 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 709,412B, BPFP=0.4503 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 720,428B, BPFP=0.4573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,828B, BPFP=0.2671 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 422,556B, BPFP=0.2682 +⌛️ [2/4] FRONTEND: Frontend time: 33.220s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.574s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549763 4.32114878 + layer.9.1 0.14520254 4.37317224 + layer.19.0 0.04746155 6.90526804 + layer.19.1 0.04383140 7.21719232 + layer.29.0 4.26247378 41.34031727 + layer.29.1 4.25497898 43.96439308 + layer.39.0 7.94138086 1565.57946051 + layer.39.1 7.86439079 1589.95173871 + ------------------------------------------------------------------------------------- + TOTAL 3.08815219 407.95658637 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3418284 +BPFP 0.2712 bits/point +EBPFP 0.2712 equivalent bits/point +MSE 407.956586 +---------------------- ---------------------------------------------------------- +Time: 66.002s Load: 1.208s, Pack+Encode: 33.220s, Decode+Unpack: 31.574s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 407.9566 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,672B, BPFP=0.1109 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,916B, BPFP=0.1091 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 409,524B, BPFP=0.2599 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 408,064B, BPFP=0.2590 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 745,264B, BPFP=0.4731 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 722,852B, BPFP=0.4588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 453,764B, BPFP=0.2880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 460,032B, BPFP=0.2920 +⌛️ [2/4] FRONTEND: Frontend time: 33.168s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.668s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11283708 4.40849479 + layer.9.1 0.11300174 4.40187467 + layer.19.0 3.22718329 6.61945419 + layer.19.1 3.22892155 7.30473828 + layer.29.0 4.26448309 49.91434941 + layer.29.1 4.25758082 55.18986635 + layer.39.0 9.82393946 2003.68248294 + layer.39.1 9.78394007 1866.15892103 + ------------------------------------------------------------------------------------- + TOTAL 4.35148589 499.71002271 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3546088 +BPFP 0.2814 bits/point +EBPFP 0.2814 equivalent bits/point +MSE 499.710023 +---------------------- ---------------------------------------------------------- +Time: 66.046s Load: 1.209s, Pack+Encode: 33.168s, Decode+Unpack: 31.668s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 499.7100 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.192s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,844B, BPFP=0.1224 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 189,232B, BPFP=0.1201 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 511,548B, BPFP=0.3247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 515,060B, BPFP=0.3269 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 910,536B, BPFP=0.5780 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 873,696B, BPFP=0.5546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 525,652B, BPFP=0.3337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 522,956B, BPFP=0.3319 +⌛️ [2/4] FRONTEND: Frontend time: 33.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.714s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14503861 4.37009307 + layer.9.1 0.14483112 4.35610287 + layer.19.0 0.11529889 7.11772958 + layer.19.1 0.11517203 7.31743074 + layer.29.0 0.11961639 52.46948123 + layer.29.1 0.11795276 51.60801410 + layer.39.0 83.84633978 2360.36756581 + layer.39.1 174.87768118 2417.85440364 + ------------------------------------------------------------------------------------- + TOTAL 32.43524135 613.18260263 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4241524 +BPFP 0.3365 bits/point +EBPFP 0.3365 equivalent bits/point +MSE 613.182603 +---------------------- ---------------------------------------------------------- +Time: 66.050s Load: 1.192s, Pack+Encode: 33.144s, Decode+Unpack: 31.714s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 613.1826 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.198s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,920B, BPFP=0.1180 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,708B, BPFP=0.1166 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 466,624B, BPFP=0.2962 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 469,100B, BPFP=0.2978 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 838,692B, BPFP=0.5324 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 845,716B, BPFP=0.5368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,576B, BPFP=0.3152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 498,412B, BPFP=0.3164 +⌛️ [2/4] FRONTEND: Frontend time: 33.055s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.682s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.81453913 4.37591721 + layer.9.1 0.14528001 4.37795000 + layer.19.0 3.26598681 6.82060702 + layer.19.1 0.04116655 6.73693685 + layer.29.0 4.28557138 50.84288674 + layer.29.1 4.28198282 47.15714373 + layer.39.0 74.89367180 1878.31491713 + layer.39.1 42.04871577 2035.89047774 + ------------------------------------------------------------------------------------- + TOTAL 16.47211428 504.31460455 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3984748 +BPFP 0.3162 bits/point +EBPFP 0.3162 equivalent bits/point +MSE 504.314605 +---------------------- ---------------------------------------------------------- +Time: 65.934s Load: 1.198s, Pack+Encode: 33.055s, Decode+Unpack: 31.682s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 504.3146 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.191s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,928B, BPFP=0.1098 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,536B, BPFP=0.1133 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 472,192B, BPFP=0.2997 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 482,568B, BPFP=0.3063 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 856,552B, BPFP=0.5437 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 862,464B, BPFP=0.5474 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 455,036B, BPFP=0.2888 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 450,540B, BPFP=0.2860 +⌛️ [2/4] FRONTEND: Frontend time: 33.180s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.641s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66710422 4.43007470 + layer.9.1 2.66812426 4.41250514 + layer.19.0 3.22059776 7.84359639 + layer.19.1 3.22546153 7.65746364 + layer.29.0 0.11226317 68.59331329 + layer.29.1 0.11257672 51.90232979 + layer.39.0 59.39237691 1863.05102372 + layer.39.1 37.52358222 1937.72440689 + ------------------------------------------------------------------------------------- + TOTAL 13.61526085 493.20183920 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3930816 +BPFP 0.3119 bits/point +EBPFP 0.3119 equivalent bits/point +MSE 493.201839 +---------------------- ---------------------------------------------------------- +Time: 66.012s Load: 1.191s, Pack+Encode: 33.180s, Decode+Unpack: 31.641s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 493.2018 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.195s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,044B, BPFP=0.1092 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 171,780B, BPFP=0.1090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 468,380B, BPFP=0.2973 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 466,948B, BPFP=0.2964 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 835,328B, BPFP=0.5302 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 840,336B, BPFP=0.5334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,760B, BPFP=0.2804 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 450,492B, BPFP=0.2859 +⌛️ [2/4] FRONTEND: Frontend time: 33.009s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.682s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519123 4.35087284 + layer.9.1 0.14511500 4.29317727 + layer.19.0 0.03974548 7.07509026 + layer.19.1 0.03981401 7.08807788 + layer.29.0 4.26343511 52.66211001 + layer.29.1 4.25610090 55.37390823 + layer.39.0 7.90972018 1607.78079298 + layer.39.1 8.05601540 1671.57572311 + ------------------------------------------------------------------------------------- + TOTAL 3.10689216 426.27496907 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3847068 +BPFP 0.3052 bits/point +EBPFP 0.3052 equivalent bits/point +MSE 426.274969 +---------------------- ---------------------------------------------------------- +Time: 65.887s Load: 1.195s, Pack+Encode: 33.009s, Decode+Unpack: 31.682s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 426.2750 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.202s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,364B, BPFP=0.1069 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 168,400B, BPFP=0.1069 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 443,560B, BPFP=0.2815 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 439,920B, BPFP=0.2792 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 990,080B, BPFP=0.6285 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 976,968B, BPFP=0.6201 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 448,756B, BPFP=0.2848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 443,676B, BPFP=0.2816 +⌛️ [2/4] FRONTEND: Frontend time: 33.339s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14586543 4.37704389 + layer.9.1 0.14572574 4.38081240 + layer.19.0 0.03953905 7.50505134 + layer.19.1 0.03760033 6.90790289 + layer.29.0 0.10448607 64.16726926 + layer.29.1 0.10697372 73.62104932 + layer.39.0 14.19073468 1738.71189470 + layer.39.1 8.92149669 1737.08904777 + ------------------------------------------------------------------------------------- + TOTAL 2.96155271 454.59500895 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4079724 +BPFP 0.3237 bits/point +EBPFP 0.3237 equivalent bits/point +MSE 454.595009 +---------------------- ---------------------------------------------------------- +Time: 66.135s Load: 1.202s, Pack+Encode: 33.339s, Decode+Unpack: 31.594s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 454.5950 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.199s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,644B, BPFP=0.1159 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 182,700B, BPFP=0.1160 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 510,564B, BPFP=0.3241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 511,712B, BPFP=0.3248 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 951,076B, BPFP=0.6037 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 933,360B, BPFP=0.5925 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,764B, BPFP=0.3026 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 484,472B, BPFP=0.3075 +⌛️ [2/4] FRONTEND: Frontend time: 33.342s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.241s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00251883 4.40814282 + layer.9.1 0.14409062 4.40710882 + layer.19.0 0.12740102 7.42877981 + layer.19.1 0.12254588 7.16843973 + layer.29.0 4.25147928 51.58670174 + layer.29.1 4.25065697 52.35530752 + layer.39.0 9.21805114 1841.23383165 + layer.39.1 9.03214690 1753.27266818 + ------------------------------------------------------------------------------------- + TOTAL 3.39361133 465.23262254 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4233292 +BPFP 0.3359 bits/point +EBPFP 0.3359 equivalent bits/point +MSE 465.232623 +---------------------- ---------------------------------------------------------- +Time: 65.782s Load: 1.199s, Pack+Encode: 33.342s, Decode+Unpack: 31.241s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 465.2326 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.191s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,228B, BPFP=0.1214 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 190,056B, BPFP=0.1206 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 546,136B, BPFP=0.3467 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 542,524B, BPFP=0.3444 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 957,752B, BPFP=0.6079 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 961,312B, BPFP=0.6102 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 523,004B, BPFP=0.3320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 519,536B, BPFP=0.3298 +⌛️ [2/4] FRONTEND: Frontend time: 33.225s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.690s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550905 4.40195624 + layer.9.1 0.14590163 4.39734097 + layer.19.0 0.12839093 7.63469961 + layer.19.1 0.12422524 7.40356310 + layer.29.0 0.11695262 59.65366022 + layer.29.1 0.11389293 63.66938678 + layer.39.0 10.18180439 2238.42053949 + layer.39.1 10.42432323 2192.28388040 + ------------------------------------------------------------------------------------- + TOTAL 2.67262500 572.23312835 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4431548 +BPFP 0.3516 bits/point +EBPFP 0.3516 equivalent bits/point +MSE 572.233128 +---------------------- ---------------------------------------------------------- +Time: 66.106s Load: 1.191s, Pack+Encode: 33.225s, Decode+Unpack: 31.690s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.2331 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.198s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,528B, BPFP=0.1146 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 177,384B, BPFP=0.1126 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 507,440B, BPFP=0.3221 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 521,136B, BPFP=0.3308 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,040,312B, BPFP=0.6603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,042,684B, BPFP=0.6618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,812B, BPFP=0.3185 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 509,360B, BPFP=0.3233 +⌛️ [2/4] FRONTEND: Frontend time: 33.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509775 4.37052279 + layer.9.1 0.14508723 4.37343915 + layer.19.0 0.11633494 7.06609776 + layer.19.1 0.11804005 6.96290967 + layer.29.0 0.15409572 75.38253575 + layer.29.1 0.14997486 70.20305899 + layer.39.0 9.23291952 2098.97660058 + layer.39.1 9.22304726 2038.28063048 + ------------------------------------------------------------------------------------- + TOTAL 2.41057467 538.20197440 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4480656 +BPFP 0.3555 bits/point +EBPFP 0.3555 equivalent bits/point +MSE 538.201974 +---------------------- ---------------------------------------------------------- +Time: 65.853s Load: 1.198s, Pack+Encode: 33.139s, Decode+Unpack: 31.516s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 538.2020 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.207s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,224B, BPFP=0.1182 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 187,092B, BPFP=0.1188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 530,596B, BPFP=0.3368 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 537,640B, BPFP=0.3413 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,011,160B, BPFP=0.6418 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,001,624B, BPFP=0.6358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 517,548B, BPFP=0.3285 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 525,572B, BPFP=0.3336 +⌛️ [2/4] FRONTEND: Frontend time: 33.025s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.750s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497060 4.39092936 + layer.9.1 0.14492971 4.38650357 + layer.19.0 0.11929473 7.22520604 + layer.19.1 0.11869117 7.15418706 + layer.29.0 0.13715227 74.31429253 + layer.29.1 0.14278979 72.72700175 + layer.39.0 9.99110525 2185.50194995 + layer.39.1 10.01170034 2123.57653559 + ------------------------------------------------------------------------------------- + TOTAL 2.60132923 559.90957573 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4497456 +BPFP 0.3568 bits/point +EBPFP 0.3568 equivalent bits/point +MSE 559.909576 +---------------------- ---------------------------------------------------------- +Time: 65.982s Load: 1.207s, Pack+Encode: 33.025s, Decode+Unpack: 31.750s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 559.9096 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,144B, BPFP=0.1156 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 179,616B, BPFP=0.1140 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 510,356B, BPFP=0.3239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 500,320B, BPFP=0.3176 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 982,084B, BPFP=0.6234 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 969,508B, BPFP=0.6154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,560B, BPFP=0.3412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 536,744B, BPFP=0.3407 +⌛️ [2/4] FRONTEND: Frontend time: 33.197s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.654s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14589856 4.40767152 + layer.9.1 0.03321603 4.39453062 + layer.19.0 0.11866178 7.12700073 + layer.19.1 0.11267978 7.07646576 + layer.29.0 0.10803594 56.87155204 + layer.29.1 0.10714094 59.78313902 + layer.39.0 11.58943751 2374.44556386 + layer.39.1 9.70079103 2372.90022749 + ------------------------------------------------------------------------------------- + TOTAL 2.73948269 610.87576888 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4398332 +BPFP 0.3490 bits/point +EBPFP 0.3490 equivalent bits/point +MSE 610.875769 +---------------------- ---------------------------------------------------------- +Time: 66.054s Load: 1.203s, Pack+Encode: 33.197s, Decode+Unpack: 31.654s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 610.8758 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.212s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,376B, BPFP=0.1120 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 180,408B, BPFP=0.1145 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 478,184B, BPFP=0.3035 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 485,680B, BPFP=0.3083 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 977,332B, BPFP=0.6204 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,011,036B, BPFP=0.6418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,120B, BPFP=0.3162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 500,244B, BPFP=0.3175 +⌛️ [2/4] FRONTEND: Frontend time: 33.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03308301 4.37178594 + layer.9.1 0.14566304 4.39279965 + layer.19.0 0.03810260 6.86335171 + layer.19.1 0.03780774 7.18202655 + layer.29.0 0.11592613 70.26804721 + layer.29.1 0.11717217 69.75711935 + layer.39.0 9.98032847 2057.62804680 + layer.39.1 9.70849498 2116.13243419 + ------------------------------------------------------------------------------------- + TOTAL 2.52207227 542.07445143 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4307380 +BPFP 0.3418 bits/point +EBPFP 0.3418 equivalent bits/point +MSE 542.074451 +---------------------- ---------------------------------------------------------- +Time: 65.866s Load: 1.212s, Pack+Encode: 33.157s, Decode+Unpack: 31.498s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 542.0745 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,040B, BPFP=0.1105 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 173,580B, BPFP=0.1102 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 457,144B, BPFP=0.2902 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 454,964B, BPFP=0.2888 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 886,428B, BPFP=0.5627 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 891,672B, BPFP=0.5660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,924B, BPFP=0.2976 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 461,044B, BPFP=0.2926 +⌛️ [2/4] FRONTEND: Frontend time: 33.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.639s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14555504 4.38994740 + layer.9.1 0.14557384 4.38333649 + layer.19.0 0.03995539 7.66358898 + layer.19.1 0.04542811 7.12344169 + layer.29.0 0.12033866 59.78816623 + layer.29.1 0.13252172 66.39230582 + layer.39.0 10.37566776 1923.91013975 + layer.39.1 9.84188447 1945.22716932 + ------------------------------------------------------------------------------------- + TOTAL 2.60586562 502.35976196 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3967796 +BPFP 0.3148 bits/point +EBPFP 0.3148 equivalent bits/point +MSE 502.359762 +---------------------- ---------------------------------------------------------- +Time: 65.997s Load: 1.203s, Pack+Encode: 33.155s, Decode+Unpack: 31.639s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 502.3598 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,632B, BPFP=0.1185 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 187,528B, BPFP=0.1190 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 520,284B, BPFP=0.3303 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 532,828B, BPFP=0.3382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,007,860B, BPFP=0.6397 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,004,176B, BPFP=0.6374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,968B, BPFP=0.3624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 570,840B, BPFP=0.3623 +⌛️ [2/4] FRONTEND: Frontend time: 32.839s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.330s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14470203 4.36024112 + layer.9.1 0.14481130 4.36311844 + layer.19.0 0.11257574 7.54328676 + layer.19.1 0.11422884 7.26615692 + layer.29.0 0.10456927 59.32361066 + layer.29.1 0.10551051 56.60626219 + layer.39.0 10.36536069 2438.88609035 + layer.39.1 11.81531702 2419.68410790 + ------------------------------------------------------------------------------------- + TOTAL 2.86338443 624.75410929 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4581116 +BPFP 0.3635 bits/point +EBPFP 0.3635 equivalent bits/point +MSE 624.754109 +---------------------- ---------------------------------------------------------- +Time: 65.372s Load: 1.203s, Pack+Encode: 32.839s, Decode+Unpack: 31.330s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 624.7541 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.195s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,652B, BPFP=0.1166 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 187,132B, BPFP=0.1188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 537,888B, BPFP=0.3414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 535,192B, BPFP=0.3397 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 905,684B, BPFP=0.5749 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 928,740B, BPFP=0.5895 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 509,204B, BPFP=0.3232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 507,624B, BPFP=0.3222 +⌛️ [2/4] FRONTEND: Frontend time: 33.164s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.717s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14530003 4.38915555 + layer.9.1 0.14546206 4.40849924 + layer.19.0 0.11891763 7.67187690 + layer.19.1 0.11677460 8.08703181 + layer.29.0 4.29725807 60.73911785 + layer.29.1 4.29692800 55.61324443 + layer.39.0 11.61914761 2016.62820929 + layer.39.1 11.22064282 2130.25901852 + ------------------------------------------------------------------------------------- + TOTAL 3.99505385 535.97451920 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4295116 +BPFP 0.3408 bits/point +EBPFP 0.3408 equivalent bits/point +MSE 535.974519 +---------------------- ---------------------------------------------------------- +Time: 66.076s Load: 1.195s, Pack+Encode: 33.164s, Decode+Unpack: 31.717s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 535.9745 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.202s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,980B, BPFP=0.1098 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 168,916B, BPFP=0.1072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 446,056B, BPFP=0.2831 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 446,288B, BPFP=0.2833 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 784,812B, BPFP=0.4982 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 777,796B, BPFP=0.4937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,956B, BPFP=0.3135 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 488,028B, BPFP=0.3098 +⌛️ [2/4] FRONTEND: Frontend time: 33.070s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.660s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67106792 4.43176091 + layer.9.1 2.67195307 4.42360533 + layer.19.0 0.08237472 6.99926306 + layer.19.1 0.08192194 6.86090982 + layer.29.0 0.11152953 43.70982796 + layer.29.1 0.11703055 45.78617058 + layer.39.0 163.01811830 2099.50243744 + layer.39.1 58.15221299 2210.69678258 + ------------------------------------------------------------------------------------- + TOTAL 28.36327613 552.80134471 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3778832 +BPFP 0.2998 bits/point +EBPFP 0.2998 equivalent bits/point +MSE 552.801345 +---------------------- ---------------------------------------------------------- +Time: 65.931s Load: 1.202s, Pack+Encode: 33.070s, Decode+Unpack: 31.660s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 552.8013 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.201s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,924B, BPFP=0.1117 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 181,976B, BPFP=0.1155 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 480,552B, BPFP=0.3050 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 484,040B, BPFP=0.3072 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 861,292B, BPFP=0.5467 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 866,700B, BPFP=0.5501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,320B, BPFP=0.3042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 474,628B, BPFP=0.3013 +⌛️ [2/4] FRONTEND: Frontend time: 33.017s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.659s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14620710 4.41859334 + layer.9.1 0.14642976 4.42146432 + layer.19.0 0.11726453 7.59340470 + layer.19.1 0.11958517 7.27578988 + layer.29.0 0.10693079 48.21074708 + layer.29.1 0.10826971 48.73768585 + layer.39.0 43.01306569 2104.17094573 + layer.39.1 17.12450997 2093.15014625 + ------------------------------------------------------------------------------------- + TOTAL 7.61028284 539.74734714 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4004432 +BPFP 0.3177 bits/point +EBPFP 0.3177 equivalent bits/point +MSE 539.747347 +---------------------- ---------------------------------------------------------- +Time: 65.877s Load: 1.201s, Pack+Encode: 33.017s, Decode+Unpack: 31.659s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 539.7473 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.200s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,880B, BPFP=0.1116 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 173,952B, BPFP=0.1104 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 455,760B, BPFP=0.2893 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 457,688B, BPFP=0.2905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 762,364B, BPFP=0.4839 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 763,428B, BPFP=0.4846 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 453,448B, BPFP=0.2878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 457,704B, BPFP=0.2905 +⌛️ [2/4] FRONTEND: Frontend time: 33.232s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.174s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03173930 4.39806427 + layer.9.1 0.03345565 4.38338124 + layer.19.0 3.26068347 6.72724359 + layer.19.1 3.26087326 6.84730968 + layer.29.0 4.24610771 45.19933174 + layer.29.1 4.24089229 43.25748497 + layer.39.0 8.81319124 1763.33685408 + layer.39.1 8.71779153 1752.58677283 + ------------------------------------------------------------------------------------- + TOTAL 4.07559181 453.34205530 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3700224 +BPFP 0.2936 bits/point +EBPFP 0.2936 equivalent bits/point +MSE 453.342055 +---------------------- ---------------------------------------------------------- +Time: 65.606s Load: 1.200s, Pack+Encode: 33.232s, Decode+Unpack: 31.174s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 453.3421 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.192s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,436B, BPFP=0.1031 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 167,464B, BPFP=0.1063 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 470,736B, BPFP=0.2988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 472,960B, BPFP=0.3002 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 897,604B, BPFP=0.5698 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 906,788B, BPFP=0.5756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,204B, BPFP=0.2934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 475,332B, BPFP=0.3017 +⌛️ [2/4] FRONTEND: Frontend time: 33.197s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.306s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078599 4.42969099 + layer.9.1 0.00079117 4.42666577 + layer.19.0 0.00795310 6.83253968 + layer.19.1 0.00811505 6.84285246 + layer.29.0 4.25797468 47.37461915 + layer.29.1 4.25504309 50.66825439 + layer.39.0 81.06806549 1931.69304517 + layer.39.1 44.82015254 1875.26486838 + ------------------------------------------------------------------------------------- + TOTAL 16.80236014 490.94156700 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4015524 +BPFP 0.3186 bits/point +EBPFP 0.3186 equivalent bits/point +MSE 490.941567 +---------------------- ---------------------------------------------------------- +Time: 65.695s Load: 1.192s, Pack+Encode: 33.197s, Decode+Unpack: 31.306s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 490.9416 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.189s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,388B, BPFP=0.1132 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,520B, BPFP=0.1114 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 535,148B, BPFP=0.3397 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 525,092B, BPFP=0.3333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 997,592B, BPFP=0.6332 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 987,480B, BPFP=0.6268 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,684B, BPFP=0.3165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 504,604B, BPFP=0.3203 +⌛️ [2/4] FRONTEND: Frontend time: 33.024s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.666s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00082351 4.42815077 + layer.9.1 0.02968625 4.41567540 + layer.19.0 0.00841222 7.10899859 + layer.19.1 0.03743129 6.99500071 + layer.29.0 4.28408194 64.85959335 + layer.29.1 4.28564945 58.25236127 + layer.39.0 8.35370986 1924.37179071 + layer.39.1 8.52557915 1858.62674683 + ------------------------------------------------------------------------------------- + TOTAL 3.19067171 491.13228970 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4402508 +BPFP 0.3493 bits/point +EBPFP 0.3493 equivalent bits/point +MSE 491.132290 +---------------------- ---------------------------------------------------------- +Time: 65.879s Load: 1.189s, Pack+Encode: 33.024s, Decode+Unpack: 31.666s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 491.1323 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.193s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,228B, BPFP=0.1144 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,668B, BPFP=0.1166 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 502,372B, BPFP=0.3189 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 505,688B, BPFP=0.3210 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 975,692B, BPFP=0.6193 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,002,088B, BPFP=0.6361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 489,468B, BPFP=0.3107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 489,132B, BPFP=0.3105 +⌛️ [2/4] FRONTEND: Frontend time: 33.265s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.389s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14529990 4.36794571 + layer.9.1 0.14524076 4.37223789 + layer.19.0 0.03780325 6.90423277 + layer.19.1 0.03783790 6.98165256 + layer.29.0 4.32098184 63.66286663 + layer.29.1 4.32100596 61.88323651 + layer.39.0 9.32673680 1982.01039974 + layer.39.1 9.31823369 1965.50974976 + ------------------------------------------------------------------------------------- + TOTAL 3.45664251 511.96154019 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4328336 +BPFP 0.3434 bits/point +EBPFP 0.3434 equivalent bits/point +MSE 511.961540 +---------------------- ---------------------------------------------------------- +Time: 65.848s Load: 1.193s, Pack+Encode: 33.265s, Decode+Unpack: 31.389s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 511.9615 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.202s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,820B, BPFP=0.1179 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 187,856B, BPFP=0.1192 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 504,284B, BPFP=0.3201 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 504,156B, BPFP=0.3200 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,030,896B, BPFP=0.6544 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,011,932B, BPFP=0.6423 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,728B, BPFP=0.3172 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 500,260B, BPFP=0.3175 +⌛️ [2/4] FRONTEND: Frontend time: 33.173s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.270s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497286 4.36567616 + layer.9.1 0.14497296 4.34980424 + layer.19.0 0.03962668 6.95954105 + layer.19.1 0.11751332 7.18304533 + layer.29.0 0.14529291 71.58630058 + layer.29.1 0.16241527 73.36547368 + layer.39.0 11.40179406 2027.31394215 + layer.39.1 13.03458244 2021.05541111 + ------------------------------------------------------------------------------------- + TOTAL 3.14889631 527.02239929 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4424932 +BPFP 0.3511 bits/point +EBPFP 0.3511 equivalent bits/point +MSE 527.022399 +---------------------- ---------------------------------------------------------- +Time: 65.645s Load: 1.202s, Pack+Encode: 33.173s, Decode+Unpack: 31.270s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 527.0224 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.200s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,428B, BPFP=0.1139 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 181,960B, BPFP=0.1155 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 549,620B, BPFP=0.3489 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 549,068B, BPFP=0.3485 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,028,352B, BPFP=0.6527 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,050,820B, BPFP=0.6670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 527,124B, BPFP=0.3346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 533,572B, BPFP=0.3387 +⌛️ [2/4] FRONTEND: Frontend time: 33.192s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.640s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14513344 4.38838592 + layer.9.1 0.03283094 4.39056470 + layer.19.0 0.11544709 7.32242114 + layer.19.1 0.11326018 7.74946554 + layer.29.0 0.14483232 63.15680858 + layer.29.1 0.14672551 64.75373233 + layer.39.0 10.02784076 2155.15014625 + layer.39.1 15.62606130 2255.88657784 + ------------------------------------------------------------------------------------- + TOTAL 3.29401644 570.34976279 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4599944 +BPFP 0.3650 bits/point +EBPFP 0.3650 equivalent bits/point +MSE 570.349763 +---------------------- ---------------------------------------------------------- +Time: 66.032s Load: 1.200s, Pack+Encode: 33.192s, Decode+Unpack: 31.640s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 570.3498 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,280B, BPFP=0.1138 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 180,040B, BPFP=0.1143 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 547,592B, BPFP=0.3476 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 554,448B, BPFP=0.3519 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,009,580B, BPFP=0.6408 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,034,692B, BPFP=0.6568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 534,092B, BPFP=0.3390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 548,084B, BPFP=0.3479 +⌛️ [2/4] FRONTEND: Frontend time: 33.098s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.424s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492303 4.37211094 + layer.9.1 0.14484742 4.33398501 + layer.19.0 0.11740684 7.90149572 + layer.19.1 0.11489933 7.55318695 + layer.29.0 0.12072669 63.51733121 + layer.29.1 0.12118037 58.61964779 + layer.39.0 10.74778980 2106.04354891 + layer.39.1 11.83662176 2272.27575561 + ------------------------------------------------------------------------------------- + TOTAL 2.91854940 565.57713277 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4587808 +BPFP 0.3640 bits/point +EBPFP 0.3640 equivalent bits/point +MSE 565.577133 +---------------------- ---------------------------------------------------------- +Time: 65.730s Load: 1.208s, Pack+Encode: 33.098s, Decode+Unpack: 31.424s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.5771 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.189s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,348B, BPFP=0.1189 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,616B, BPFP=0.1172 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 582,580B, BPFP=0.3698 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 581,952B, BPFP=0.3694 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,108,308B, BPFP=0.7035 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,079,260B, BPFP=0.6851 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 564,224B, BPFP=0.3581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 562,048B, BPFP=0.3568 +⌛️ [2/4] FRONTEND: Frontend time: 33.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.644s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14465000 4.35503046 + layer.9.1 0.14489275 4.48698160 + layer.19.0 0.11978787 8.27929307 + layer.19.1 0.12819003 7.99474681 + layer.29.0 0.12519148 73.11692091 + layer.29.1 0.13018718 68.80504550 + layer.39.0 10.77894586 2345.36967826 + layer.39.1 10.25834823 2374.16948326 + ------------------------------------------------------------------------------------- + TOTAL 2.72877418 610.82214748 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4850336 +BPFP 0.3848 bits/point +EBPFP 0.3848 equivalent bits/point +MSE 610.822147 +---------------------- ---------------------------------------------------------- +Time: 65.998s Load: 1.189s, Pack+Encode: 33.165s, Decode+Unpack: 31.644s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 610.8221 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.197s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,428B, BPFP=0.1101 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,840B, BPFP=0.1116 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 470,924B, BPFP=0.2989 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 481,552B, BPFP=0.3057 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 830,068B, BPFP=0.5269 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 848,516B, BPFP=0.5386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,952B, BPFP=0.3046 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 476,928B, BPFP=0.3027 +⌛️ [2/4] FRONTEND: Frontend time: 33.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.617s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14553812 4.41469535 + layer.9.1 0.14559401 4.40499256 + layer.19.0 0.04492324 7.40107044 + layer.19.1 0.04213941 7.36642580 + layer.29.0 4.25320263 50.13352596 + layer.29.1 4.25391672 50.08516311 + layer.39.0 8.72311137 1817.12902177 + layer.39.1 8.87262096 1858.91062723 + ------------------------------------------------------------------------------------- + TOTAL 3.31013081 474.98069028 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 3937208 +BPFP 0.3124 bits/point +EBPFP 0.3124 equivalent bits/point +MSE 474.980690 +---------------------- ---------------------------------------------------------- +Time: 65.954s Load: 1.197s, Pack+Encode: 33.139s, Decode+Unpack: 31.617s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 474.9807 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,080B, BPFP=0.1168 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,588B, BPFP=0.1165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 521,224B, BPFP=0.3308 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 521,684B, BPFP=0.3311 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 968,020B, BPFP=0.6145 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 968,008B, BPFP=0.6144 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,888B, BPFP=0.3256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 505,224B, BPFP=0.3207 +⌛️ [2/4] FRONTEND: Frontend time: 33.219s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.385s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14518122 4.38326476 + layer.9.1 0.14529820 4.37891069 + layer.19.0 0.11833418 7.12989519 + layer.19.1 0.12038008 7.07123225 + layer.29.0 4.31360161 72.04323408 + layer.29.1 4.31792870 76.09054578 + layer.39.0 9.40764201 2211.97757556 + layer.39.1 11.30764416 2081.09246019 + ------------------------------------------------------------------------------------- + TOTAL 3.73450127 558.02088981 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4364716 +BPFP 0.3463 bits/point +EBPFP 0.3463 equivalent bits/point +MSE 558.020890 +---------------------- ---------------------------------------------------------- +Time: 65.811s Load: 1.206s, Pack+Encode: 33.219s, Decode+Unpack: 31.385s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 558.0209 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,968B, BPFP=0.1187 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 187,256B, BPFP=0.1189 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 540,296B, BPFP=0.3430 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 545,660B, BPFP=0.3464 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 994,088B, BPFP=0.6310 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 998,588B, BPFP=0.6339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,120B, BPFP=0.3130 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 497,072B, BPFP=0.3155 +⌛️ [2/4] FRONTEND: Frontend time: 33.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.386s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00315138 4.43410092 + layer.9.1 0.00505826 4.43049903 + layer.19.0 0.09147678 7.76244871 + layer.19.1 0.09143778 7.56456230 + layer.29.0 0.11015094 60.76439612 + layer.29.1 0.11338039 60.50959742 + layer.39.0 9.14784464 2053.44523887 + layer.39.1 8.98944348 1938.92882678 + ------------------------------------------------------------------------------------- + TOTAL 2.31899296 517.22995877 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4443048 +BPFP 0.3525 bits/point +EBPFP 0.3525 equivalent bits/point +MSE 517.229959 +---------------------- ---------------------------------------------------------- +Time: 65.766s Load: 1.215s, Pack+Encode: 33.165s, Decode+Unpack: 31.386s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 517.2300 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.197s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,844B, BPFP=0.1288 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 201,240B, BPFP=0.1277 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 603,496B, BPFP=0.3831 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 591,272B, BPFP=0.3753 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,087,204B, BPFP=0.6901 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,075,968B, BPFP=0.6830 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,532B, BPFP=0.3679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 569,256B, BPFP=0.3613 +⌛️ [2/4] FRONTEND: Frontend time: 33.274s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.673s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14591913 4.43011120 + layer.9.1 0.03347605 4.41546942 + layer.19.0 0.12173996 7.74581763 + layer.19.1 0.12099332 7.62679253 + layer.29.0 0.11078974 61.83781382 + layer.29.1 0.11776269 68.38028112 + layer.39.0 10.17800795 2231.26681833 + layer.39.1 9.88744998 2183.24033149 + ------------------------------------------------------------------------------------- + TOTAL 2.58951735 571.11792944 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4910812 +BPFP 0.3896 bits/point +EBPFP 0.3896 equivalent bits/point +MSE 571.117929 +---------------------- ---------------------------------------------------------- +Time: 66.144s Load: 1.197s, Pack+Encode: 33.274s, Decode+Unpack: 31.673s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 571.1179 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,692B, BPFP=0.1058 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 165,244B, BPFP=0.1049 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 495,936B, BPFP=0.3148 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 493,340B, BPFP=0.3131 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,008,540B, BPFP=0.6402 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,013,372B, BPFP=0.6432 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,508B, BPFP=0.2942 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 473,160B, BPFP=0.3003 +⌛️ [2/4] FRONTEND: Frontend time: 33.094s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66405266 4.50303664 + layer.9.1 2.66543197 4.48235110 + layer.19.0 3.22131407 7.65227583 + layer.19.1 3.22426883 7.79305254 + layer.29.0 4.27224607 62.79028884 + layer.29.1 4.27784520 64.34681711 + layer.39.0 8.94937744 1796.47578811 + layer.39.1 8.82170070 1775.32239194 + ------------------------------------------------------------------------------------- + TOTAL 4.76202962 465.42075026 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4279792 +BPFP 0.3396 bits/point +EBPFP 0.3396 equivalent bits/point +MSE 465.420750 +---------------------- ---------------------------------------------------------- +Time: 65.739s Load: 1.206s, Pack+Encode: 33.094s, Decode+Unpack: 31.439s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 465.4208 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.198s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,532B, BPFP=0.1095 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 176,348B, BPFP=0.1119 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 499,996B, BPFP=0.3174 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 503,112B, BPFP=0.3194 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 972,712B, BPFP=0.6174 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 976,456B, BPFP=0.6198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,504B, BPFP=0.3152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 496,252B, BPFP=0.3150 +⌛️ [2/4] FRONTEND: Frontend time: 33.278s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.277s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00104424 4.46553277 + layer.9.1 0.00091568 4.48150117 + layer.19.0 0.08171424 7.37553573 + layer.19.1 0.08373584 7.18441067 + layer.29.0 4.26071267 56.52365839 + layer.29.1 4.26438533 60.30973757 + layer.39.0 8.39843369 1920.88982775 + layer.39.1 8.51949380 1918.42996425 + ------------------------------------------------------------------------------------- + TOTAL 3.20130444 497.45752104 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4293912 +BPFP 0.3407 bits/point +EBPFP 0.3407 equivalent bits/point +MSE 497.457521 +---------------------- ---------------------------------------------------------- +Time: 65.753s Load: 1.198s, Pack+Encode: 33.278s, Decode+Unpack: 31.277s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 497.4575 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.205s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,164B, BPFP=0.1194 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 182,332B, BPFP=0.1157 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 533,384B, BPFP=0.3386 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 532,320B, BPFP=0.3379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,147,332B, BPFP=0.7283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,131,560B, BPFP=0.7183 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,480B, BPFP=0.3202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 502,348B, BPFP=0.3189 +⌛️ [2/4] FRONTEND: Frontend time: 33.190s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.702s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14622840 4.44094923 + layer.9.1 0.03344178 4.40323494 + layer.19.0 0.12675888 8.07483763 + layer.19.1 0.12382618 7.42403378 + layer.29.0 0.12223263 66.05062764 + layer.29.1 0.12797405 63.63792858 + layer.39.0 10.69978368 1985.96392590 + layer.39.1 8.63538768 2000.66720832 + ------------------------------------------------------------------------------------- + TOTAL 2.50195416 517.58284325 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4721920 +BPFP 0.3747 bits/point +EBPFP 0.3747 equivalent bits/point +MSE 517.582843 +---------------------- ---------------------------------------------------------- +Time: 66.098s Load: 1.205s, Pack+Encode: 33.190s, Decode+Unpack: 31.702s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 517.5828 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,892B, BPFP=0.1205 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 188,080B, BPFP=0.1194 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 529,244B, BPFP=0.3359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 536,504B, BPFP=0.3405 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 997,712B, BPFP=0.6333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 979,964B, BPFP=0.6220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 524,428B, BPFP=0.3329 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 534,684B, BPFP=0.3394 +⌛️ [2/4] FRONTEND: Frontend time: 32.894s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.717s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492994 4.37205190 + layer.9.1 0.14498602 4.36995913 + layer.19.0 0.12957112 7.20589060 + layer.19.1 0.13054295 7.56218326 + layer.29.0 0.16610158 56.22818492 + layer.29.1 0.14872770 62.06059067 + layer.39.0 16.52878844 2168.97335067 + layer.39.1 24.55764797 2326.35781605 + ------------------------------------------------------------------------------------- + TOTAL 5.24391197 579.64125340 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 4480508 +BPFP 0.3555 bits/point +EBPFP 0.3555 equivalent bits/point +MSE 579.641253 +---------------------- ---------------------------------------------------------- +Time: 65.814s Load: 1.203s, Pack+Encode: 32.894s, Decode+Unpack: 31.717s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 579.6413 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.3332 bits/point +Avg EBPFP 0.3332 equivalent bits/point +Avg MSE 539.597033 +Avg Time 65.809s +------------------------ ---------------------------- diff --git a/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..a532c02a325e48dc862d18c798023b0d2ab9a3f0 --- /dev/null +++ b/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 333 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.203s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,352B, BPFP=0.2072 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 335,412B, BPFP=0.2129 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 805,000B, BPFP=0.5110 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 792,008B, BPFP=0.5027 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,021,568B, BPFP=0.6484 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,034,184B, BPFP=0.6564 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 715,772B, BPFP=0.4543 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 731,028B, BPFP=0.4640 +⌛️ [2/4] FRONTEND: Frontend time: 33.752s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561056 0.33378685 + layer.9.1 0.14522085 0.33419202 + layer.19.0 3.25142184 24.76428187 + layer.19.1 3.25206135 16.51732359 + layer.29.0 4.23946030 28.87532753 + layer.29.1 4.24539299 29.70238869 + layer.39.0 32.17105490 1427.60919727 + layer.39.1 19.15684032 1415.28534287 + ------------------------------------------------------------------------------------- + TOTAL 8.32588289 367.92773009 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5761324 +BPFP 0.4571 bits/point +EBPFP 0.4571 equivalent bits/point +MSE 367.927730 +---------------------- ---------------------------------------------------------- +Time: 66.550s Load: 1.203s, Pack+Encode: 33.752s, Decode+Unpack: 31.594s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 367.9277 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.233s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 379,276B, BPFP=0.2407 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 371,644B, BPFP=0.2359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 946,764B, BPFP=0.6010 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 944,264B, BPFP=0.5994 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,169,852B, BPFP=0.7426 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,171,080B, BPFP=0.7433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 827,732B, BPFP=0.5254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 892,936B, BPFP=0.5668 +⌛️ [2/4] FRONTEND: Frontend time: 33.282s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.795s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03307773 4.34880388 + layer.9.1 0.03291117 4.31006986 + layer.19.0 0.04156009 9.08223691 + layer.19.1 0.03760627 12.22613849 + layer.29.0 4.28582750 42.68678908 + layer.29.1 4.28551552 35.46214352 + layer.39.0 9.83402183 1885.89762756 + layer.39.1 9.85397836 3196.22716932 + ------------------------------------------------------------------------------------- + TOTAL 3.55056231 648.78012233 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6703548 +BPFP 0.5319 bits/point +EBPFP 0.5319 equivalent bits/point +MSE 648.780122 +---------------------- ---------------------------------------------------------- +Time: 66.310s Load: 1.233s, Pack+Encode: 33.282s, Decode+Unpack: 31.795s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 648.7801 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.233s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 422,736B, BPFP=0.2683 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 417,008B, BPFP=0.2647 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 980,828B, BPFP=0.6226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 971,064B, BPFP=0.6164 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,233,296B, BPFP=0.7828 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,236,324B, BPFP=0.7848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 812,972B, BPFP=0.5160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 850,180B, BPFP=0.5397 +⌛️ [2/4] FRONTEND: Frontend time: 33.433s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.821s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083900 0.34887862 + layer.9.1 0.00259629 0.34876659 + layer.19.0 0.00955961 7.79629484 + layer.19.1 0.08538111 12.56264345 + layer.29.0 0.11631418 33.51974580 + layer.29.1 0.11200302 38.86018748 + layer.39.0 14.47657393 2508.39259019 + layer.39.1 13.08093694 3058.97400065 + ------------------------------------------------------------------------------------- + TOTAL 3.48552551 707.60038845 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6924408 +BPFP 0.5494 bits/point +EBPFP 0.5494 equivalent bits/point +MSE 707.600388 +---------------------- ---------------------------------------------------------- +Time: 66.487s Load: 1.233s, Pack+Encode: 33.433s, Decode+Unpack: 31.821s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 707.6004 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.218s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,932B, BPFP=0.2259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 350,280B, BPFP=0.2223 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 859,660B, BPFP=0.5457 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 851,800B, BPFP=0.5407 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,072,828B, BPFP=0.6810 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,066,804B, BPFP=0.6772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 780,568B, BPFP=0.4955 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 753,016B, BPFP=0.4780 +⌛️ [2/4] FRONTEND: Frontend time: 33.461s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.740s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.70169554 0.35761198 + layer.9.1 0.03294074 0.36553537 + layer.19.0 3.25671692 18.05472432 + layer.19.1 3.25834093 20.13077495 + layer.29.0 0.10810242 40.96497451 + layer.29.1 0.10661203 64.19389320 + layer.39.0 8.95005916 1795.41160221 + layer.39.1 8.98756017 1726.24276893 + ------------------------------------------------------------------------------------- + TOTAL 3.42525349 458.21523568 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6090888 +BPFP 0.4833 bits/point +EBPFP 0.4833 equivalent bits/point +MSE 458.215236 +---------------------- ---------------------------------------------------------- +Time: 66.419s Load: 1.218s, Pack+Encode: 33.461s, Decode+Unpack: 31.740s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 458.2152 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.222s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 418,164B, BPFP=0.2654 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 406,212B, BPFP=0.2578 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 934,652B, BPFP=0.5933 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 932,300B, BPFP=0.5918 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,194,536B, BPFP=0.7582 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,201,376B, BPFP=0.7626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 782,248B, BPFP=0.4965 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 790,512B, BPFP=0.5018 +⌛️ [2/4] FRONTEND: Frontend time: 33.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.858s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527870 3.04860406 + layer.9.1 0.14521496 4.30527877 + layer.19.0 0.03964342 10.34702087 + layer.19.1 0.03956446 7.61345263 + layer.29.0 0.12258449 40.92318258 + layer.29.1 0.12735008 44.97709823 + layer.39.0 32.94776263 2624.88852779 + layer.39.1 29.25669534 2502.20311992 + ------------------------------------------------------------------------------------- + TOTAL 7.85301176 654.78828561 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6660000 +BPFP 0.5284 bits/point +EBPFP 0.5284 equivalent bits/point +MSE 654.788286 +---------------------- ---------------------------------------------------------- +Time: 66.604s Load: 1.222s, Pack+Encode: 33.524s, Decode+Unpack: 31.858s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 654.7883 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 370,504B, BPFP=0.2352 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 385,584B, BPFP=0.2447 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 852,196B, BPFP=0.5409 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 843,528B, BPFP=0.5354 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,074,500B, BPFP=0.6820 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,072,324B, BPFP=0.6807 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 751,860B, BPFP=0.4772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 751,768B, BPFP=0.4772 +⌛️ [2/4] FRONTEND: Frontend time: 33.330s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.761s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66816044 4.41776436 + layer.9.1 2.66817504 3.14056216 + layer.19.0 3.22262959 14.31762243 + layer.19.1 3.22037432 15.90654706 + layer.29.0 4.30448692 57.19752397 + layer.29.1 4.31085282 70.56881703 + layer.39.0 38.33931691 2207.02729932 + layer.39.1 57.25219370 2156.51007475 + ------------------------------------------------------------------------------------- + TOTAL 14.49827372 566.13577638 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6102264 +BPFP 0.4842 bits/point +EBPFP 0.4842 equivalent bits/point +MSE 566.135776 +---------------------- ---------------------------------------------------------- +Time: 66.318s Load: 1.227s, Pack+Encode: 33.330s, Decode+Unpack: 31.761s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 566.1358 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.222s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 396,232B, BPFP=0.2515 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 392,228B, BPFP=0.2490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 877,648B, BPFP=0.5571 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 889,128B, BPFP=0.5644 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,101,632B, BPFP=0.6993 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,093,192B, BPFP=0.6939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 739,752B, BPFP=0.4696 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 746,444B, BPFP=0.4738 +⌛️ [2/4] FRONTEND: Frontend time: 33.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.748s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332920 0.46209858 + layer.9.1 0.00092169 4.43614132 + layer.19.0 3.23006092 6.58171959 + layer.19.1 3.23257961 5.86905049 + layer.29.0 4.28548854 37.49148420 + layer.29.1 4.27808990 61.84897526 + layer.39.0 10.57841825 1605.04192395 + layer.39.1 20.33118703 1902.14315892 + ------------------------------------------------------------------------------------- + TOTAL 5.75625939 452.98431904 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6236256 +BPFP 0.4948 bits/point +EBPFP 0.4948 equivalent bits/point +MSE 452.984319 +---------------------- ---------------------------------------------------------- +Time: 66.556s Load: 1.222s, Pack+Encode: 33.586s, Decode+Unpack: 31.748s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 452.9843 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.212s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 407,788B, BPFP=0.2588 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 417,968B, BPFP=0.2653 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 886,324B, BPFP=0.5626 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 892,268B, BPFP=0.5664 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,068,196B, BPFP=0.6780 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,063,648B, BPFP=0.6752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 729,084B, BPFP=0.4628 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 711,260B, BPFP=0.4515 +⌛️ [2/4] FRONTEND: Frontend time: 33.323s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.671s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14443954 0.35621553 + layer.9.1 0.14435121 0.39565496 + layer.19.0 0.03807715 21.37064816 + layer.19.1 0.03781311 20.17957959 + layer.29.0 0.10781899 41.41621811 + layer.29.1 0.10618912 77.53465226 + layer.39.0 9.30898666 2090.00081248 + layer.39.1 9.83625107 2362.94832629 + ------------------------------------------------------------------------------------- + TOTAL 2.46549086 576.77526342 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6176536 +BPFP 0.4901 bits/point +EBPFP 0.4901 equivalent bits/point +MSE 576.775263 +---------------------- ---------------------------------------------------------- +Time: 66.206s Load: 1.212s, Pack+Encode: 33.323s, Decode+Unpack: 31.671s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 576.7753 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 432,240B, BPFP=0.2744 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 421,940B, BPFP=0.2678 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 970,668B, BPFP=0.6161 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 968,592B, BPFP=0.6148 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,227,396B, BPFP=0.7791 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,221,160B, BPFP=0.7751 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 820,132B, BPFP=0.5206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 816,856B, BPFP=0.5185 +⌛️ [2/4] FRONTEND: Frontend time: 33.754s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.766s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14543928 0.36843649 + layer.9.1 0.14562574 0.36159079 + layer.19.0 0.11552505 16.32516072 + layer.19.1 0.12052174 19.74779107 + layer.29.0 0.10841144 88.19888487 + layer.29.1 0.10845811 73.56371872 + layer.39.0 9.17501701 2082.16038349 + layer.39.1 9.20635778 2246.61342216 + ------------------------------------------------------------------------------------- + TOTAL 2.39066952 565.91742354 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6878984 +BPFP 0.5458 bits/point +EBPFP 0.5458 equivalent bits/point +MSE 565.917424 +---------------------- ---------------------------------------------------------- +Time: 66.744s Load: 1.225s, Pack+Encode: 33.754s, Decode+Unpack: 31.766s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.9174 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 400,576B, BPFP=0.2543 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 383,392B, BPFP=0.2434 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 793,552B, BPFP=0.5037 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 832,320B, BPFP=0.5283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 984,612B, BPFP=0.6250 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,010,912B, BPFP=0.6417 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 698,456B, BPFP=0.4433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 697,148B, BPFP=0.4425 +⌛️ [2/4] FRONTEND: Frontend time: 33.479s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.713s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.78226592 0.37059968 + layer.9.1 2.78427046 0.34803956 + layer.19.0 3.22580366 17.05116337 + layer.19.1 3.22969594 9.24746608 + layer.29.0 4.29525448 70.27927974 + layer.29.1 0.11349234 55.15666132 + layer.39.0 8.89338553 1316.77786805 + layer.39.1 8.88767087 1294.38609035 + ------------------------------------------------------------------------------------- + TOTAL 4.27647990 345.45214602 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5800968 +BPFP 0.4603 bits/point +EBPFP 0.4603 equivalent bits/point +MSE 345.452146 +---------------------- ---------------------------------------------------------- +Time: 66.407s Load: 1.215s, Pack+Encode: 33.479s, Decode+Unpack: 31.713s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 345.4521 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.249s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,664B, BPFP=0.2581 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 395,272B, BPFP=0.2509 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 850,636B, BPFP=0.5399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 884,192B, BPFP=0.5612 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,009,988B, BPFP=0.6411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,072,980B, BPFP=0.6811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 668,000B, BPFP=0.4240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 702,920B, BPFP=0.4462 +⌛️ [2/4] FRONTEND: Frontend time: 33.414s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.792s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14514284 0.41172257 + layer.9.1 0.14518188 4.41974541 + layer.19.0 0.04057091 30.74384039 + layer.19.1 0.04041447 35.16479881 + layer.29.0 4.25641542 166.27554233 + layer.29.1 4.26613502 44.17994902 + layer.39.0 12.58558458 1982.50081248 + layer.39.1 8.96866240 2384.86252844 + ------------------------------------------------------------------------------------- + TOTAL 3.80601344 581.06986743 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5990652 +BPFP 0.4753 bits/point +EBPFP 0.4753 equivalent bits/point +MSE 581.069867 +---------------------- ---------------------------------------------------------- +Time: 66.455s Load: 1.249s, Pack+Encode: 33.414s, Decode+Unpack: 31.792s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 581.0699 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.218s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 394,336B, BPFP=0.2503 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 385,872B, BPFP=0.2449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 854,332B, BPFP=0.5423 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 872,516B, BPFP=0.5538 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,064,288B, BPFP=0.6756 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,081,232B, BPFP=0.6863 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 730,680B, BPFP=0.4638 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 730,096B, BPFP=0.4634 +⌛️ [2/4] FRONTEND: Frontend time: 33.374s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.684s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67015388 4.30540318 + layer.9.1 0.00076871 0.35971582 + layer.19.0 3.22151687 8.36361322 + layer.19.1 3.22388957 12.30370491 + layer.29.0 4.24084786 44.99222558 + layer.29.1 4.24602234 29.46776487 + layer.39.0 7.87160790 2242.22651934 + layer.39.1 9.85764150 1766.33262918 + ------------------------------------------------------------------------------------- + TOTAL 4.41655608 513.54394701 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6113352 +BPFP 0.4851 bits/point +EBPFP 0.4851 equivalent bits/point +MSE 513.543947 +---------------------- ---------------------------------------------------------- +Time: 66.276s Load: 1.218s, Pack+Encode: 33.374s, Decode+Unpack: 31.684s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 513.5439 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.224s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 421,772B, BPFP=0.2677 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 424,868B, BPFP=0.2697 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 955,648B, BPFP=0.6066 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 962,368B, BPFP=0.6109 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,219,512B, BPFP=0.7741 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,235,148B, BPFP=0.7840 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 864,412B, BPFP=0.5487 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 908,004B, BPFP=0.5764 +⌛️ [2/4] FRONTEND: Frontend time: 33.341s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.721s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00068237 0.35067068 + layer.9.1 0.00070576 0.35150061 + layer.19.0 0.00823322 15.08009526 + layer.19.1 0.08594799 16.10828704 + layer.29.0 0.12200666 79.80015031 + layer.29.1 0.12451052 43.23849833 + layer.39.0 55.99513528 4272.34806630 + layer.39.1 28.81185256 4777.58336042 + ------------------------------------------------------------------------------------- + TOTAL 10.64363429 1150.60757862 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6991732 +BPFP 0.5548 bits/point +EBPFP 0.5548 equivalent bits/point +MSE 1150.607579 +---------------------- ---------------------------------------------------------- +Time: 66.286s Load: 1.224s, Pack+Encode: 33.341s, Decode+Unpack: 31.721s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1150.6076 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 417,908B, BPFP=0.2653 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 410,888B, BPFP=0.2608 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 885,272B, BPFP=0.5619 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 891,320B, BPFP=0.5658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,067,804B, BPFP=0.6778 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,070,124B, BPFP=0.6793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 720,808B, BPFP=0.4575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 739,240B, BPFP=0.4692 +⌛️ [2/4] FRONTEND: Frontend time: 33.339s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.808s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14556937 0.35096988 + layer.9.1 0.03327741 0.37508113 + layer.19.0 0.11590617 22.23489549 + layer.19.1 0.11733878 23.09141666 + layer.29.0 0.11334742 31.62934423 + layer.29.1 4.29039579 32.76486330 + layer.39.0 9.10722066 1451.77933052 + layer.39.1 44.52401893 1475.98375041 + ------------------------------------------------------------------------------------- + TOTAL 7.30588432 379.77620645 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6203364 +BPFP 0.4922 bits/point +EBPFP 0.4922 equivalent bits/point +MSE 379.776206 +---------------------- ---------------------------------------------------------- +Time: 66.373s Load: 1.225s, Pack+Encode: 33.339s, Decode+Unpack: 31.808s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 379.7762 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.210s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 449,404B, BPFP=0.2853 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 455,620B, BPFP=0.2892 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 957,172B, BPFP=0.6076 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 939,268B, BPFP=0.5962 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,154,916B, BPFP=0.7331 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,147,368B, BPFP=0.7283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,208B, BPFP=0.4381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 694,044B, BPFP=0.4405 +⌛️ [2/4] FRONTEND: Frontend time: 33.310s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.737s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11318631 4.43045682 + layer.9.1 0.11319129 3.07640229 + layer.19.0 0.00665199 12.55193776 + layer.19.1 0.00853768 18.07329328 + layer.29.0 4.27225940 43.27594349 + layer.29.1 4.27324961 57.90602657 + layer.39.0 14.80262837 1595.86057849 + layer.39.1 16.56649765 2954.12089698 + ------------------------------------------------------------------------------------- + TOTAL 5.01952529 586.16194196 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6488000 +BPFP 0.5148 bits/point +EBPFP 0.5148 equivalent bits/point +MSE 586.161942 +---------------------- ---------------------------------------------------------- +Time: 66.258s Load: 1.210s, Pack+Encode: 33.310s, Decode+Unpack: 31.737s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 586.1619 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.210s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,348B, BPFP=0.2617 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 402,768B, BPFP=0.2557 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 897,304B, BPFP=0.5696 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 917,124B, BPFP=0.5821 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,106,852B, BPFP=0.7026 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,128,020B, BPFP=0.7160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 740,604B, BPFP=0.4701 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 738,216B, BPFP=0.4686 +⌛️ [2/4] FRONTEND: Frontend time: 33.677s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.797s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00065117 0.35229111 + layer.9.1 0.00066201 0.39288201 + layer.19.0 0.00984582 20.70436403 + layer.19.1 0.01156107 11.65992774 + layer.29.0 4.26547583 67.48745227 + layer.29.1 4.26296603 50.88547591 + layer.39.0 11.21169412 1776.29200520 + layer.39.1 9.31977106 1591.13893403 + ------------------------------------------------------------------------------------- + TOTAL 3.63532839 439.86416654 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6343236 +BPFP 0.5033 bits/point +EBPFP 0.5033 equivalent bits/point +MSE 439.864167 +---------------------- ---------------------------------------------------------- +Time: 66.684s Load: 1.210s, Pack+Encode: 33.677s, Decode+Unpack: 31.797s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 439.8642 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 371,648B, BPFP=0.2359 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 367,480B, BPFP=0.2333 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 839,396B, BPFP=0.5328 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 824,832B, BPFP=0.5236 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,021,680B, BPFP=0.6485 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,014,892B, BPFP=0.6442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 672,196B, BPFP=0.4267 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 674,364B, BPFP=0.4281 +⌛️ [2/4] FRONTEND: Frontend time: 33.330s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.760s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00085355 4.31985580 + layer.9.1 0.00085581 4.46169349 + layer.19.0 0.00808159 27.43115504 + layer.19.1 0.00635426 25.65501097 + layer.29.0 4.24551200 46.73668549 + layer.29.1 4.24803037 43.77472477 + layer.39.0 9.19283951 1216.23163796 + layer.39.1 9.46657027 1408.21871953 + ------------------------------------------------------------------------------------- + TOTAL 3.39613717 347.10368538 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5786488 +BPFP 0.4591 bits/point +EBPFP 0.4591 equivalent bits/point +MSE 347.103685 +---------------------- ---------------------------------------------------------- +Time: 66.311s Load: 1.221s, Pack+Encode: 33.330s, Decode+Unpack: 31.760s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 347.1037 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 384,152B, BPFP=0.2438 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 393,268B, BPFP=0.2496 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 918,940B, BPFP=0.5833 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 913,456B, BPFP=0.5798 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,164,788B, BPFP=0.7393 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,159,824B, BPFP=0.7362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 798,452B, BPFP=0.5068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 781,248B, BPFP=0.4959 +⌛️ [2/4] FRONTEND: Frontend time: 33.710s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.736s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67056234 4.39654658 + layer.9.1 2.67147828 3.10463215 + layer.19.0 0.00618387 16.90268651 + layer.19.1 0.08383032 11.85556777 + layer.29.0 4.28489822 40.91998852 + layer.29.1 4.28470970 37.01292858 + layer.39.0 10.15376305 2781.16867078 + layer.39.1 8.47863686 2027.01754956 + ------------------------------------------------------------------------------------- + TOTAL 4.07925783 615.29732131 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6514128 +BPFP 0.5169 bits/point +EBPFP 0.5169 equivalent bits/point +MSE 615.297321 +---------------------- ---------------------------------------------------------- +Time: 66.669s Load: 1.223s, Pack+Encode: 33.710s, Decode+Unpack: 31.736s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.2973 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 386,608B, BPFP=0.2454 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 389,032B, BPFP=0.2469 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 944,692B, BPFP=0.5996 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 923,744B, BPFP=0.5863 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,175,212B, BPFP=0.7460 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,180,596B, BPFP=0.7494 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 776,148B, BPFP=0.4927 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 743,588B, BPFP=0.4720 +⌛️ [2/4] FRONTEND: Frontend time: 33.361s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.754s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67132404 3.04755418 + layer.9.1 2.67117709 4.30817862 + layer.19.0 0.00597838 10.31703275 + layer.19.1 0.00605309 10.31676488 + layer.29.0 4.29273040 37.73938698 + layer.29.1 4.29206328 32.15754235 + layer.39.0 9.96127074 2182.02811180 + layer.39.1 10.21295854 1975.76746831 + ------------------------------------------------------------------------------------- + TOTAL 4.26419445 531.96025499 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6519620 +BPFP 0.5173 bits/point +EBPFP 0.5173 equivalent bits/point +MSE 531.960255 +---------------------- ---------------------------------------------------------- +Time: 66.341s Load: 1.227s, Pack+Encode: 33.361s, Decode+Unpack: 31.754s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 531.9603 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.218s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 385,336B, BPFP=0.2446 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 397,000B, BPFP=0.2520 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 883,088B, BPFP=0.5605 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 877,188B, BPFP=0.5568 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,039,696B, BPFP=0.6599 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,025,456B, BPFP=0.6509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 682,824B, BPFP=0.4334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 692,076B, BPFP=0.4393 +⌛️ [2/4] FRONTEND: Frontend time: 33.307s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.759s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568686 0.35128452 + layer.9.1 0.14558674 4.35187384 + layer.19.0 0.00960369 26.02628880 + layer.19.1 0.03847206 31.34343263 + layer.29.0 4.24438723 35.28586336 + layer.29.1 4.24578970 35.60070940 + layer.39.0 9.23757985 1318.07848554 + layer.39.1 9.43674592 1458.98570036 + ------------------------------------------------------------------------------------- + TOTAL 3.43798151 363.75295480 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5982664 +BPFP 0.4747 bits/point +EBPFP 0.4747 equivalent bits/point +MSE 363.752955 +---------------------- ---------------------------------------------------------- +Time: 66.285s Load: 1.218s, Pack+Encode: 33.307s, Decode+Unpack: 31.759s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 363.7530 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.231s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 432,596B, BPFP=0.2746 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 431,960B, BPFP=0.2742 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 995,256B, BPFP=0.6317 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 986,000B, BPFP=0.6259 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,218,156B, BPFP=0.7732 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,204,724B, BPFP=0.7647 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 776,372B, BPFP=0.4928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 802,704B, BPFP=0.5095 +⌛️ [2/4] FRONTEND: Frontend time: 33.482s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.770s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11307967 4.30162261 + layer.9.1 0.00073224 3.07809485 + layer.19.0 0.08207503 12.56875229 + layer.19.1 0.08214869 5.58364732 + layer.29.0 4.26728487 37.67336285 + layer.29.1 4.26774951 31.83446996 + layer.39.0 12.81553410 2786.28209295 + layer.39.1 23.05196315 3285.07409815 + ------------------------------------------------------------------------------------- + TOTAL 5.58507091 770.79951762 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6847768 +BPFP 0.5433 bits/point +EBPFP 0.5433 equivalent bits/point +MSE 770.799518 +---------------------- ---------------------------------------------------------- +Time: 66.483s Load: 1.231s, Pack+Encode: 33.482s, Decode+Unpack: 31.770s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 770.7995 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 538,056B, BPFP=0.3415 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 541,120B, BPFP=0.3435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,112,248B, BPFP=0.7060 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,116,756B, BPFP=0.7089 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,425,104B, BPFP=0.9046 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,431,724B, BPFP=0.9088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 798,416B, BPFP=0.5068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 784,884B, BPFP=0.4982 +⌛️ [2/4] FRONTEND: Frontend time: 33.792s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.850s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519844 4.48679308 + layer.9.1 0.14499054 0.40812596 + layer.19.0 0.12156012 14.30605602 + layer.19.1 0.12030756 17.95531235 + layer.29.0 0.12020218 494.50052811 + layer.29.1 0.12115470 375.36114722 + layer.39.0 8.85439666 2237.36870328 + layer.39.1 8.75438231 1729.14104647 + ------------------------------------------------------------------------------------- + TOTAL 2.29777406 609.19096406 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7748308 +BPFP 0.6148 bits/point +EBPFP 0.6148 equivalent bits/point +MSE 609.190964 +---------------------- ---------------------------------------------------------- +Time: 66.865s Load: 1.223s, Pack+Encode: 33.792s, Decode+Unpack: 31.850s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 609.1910 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 548,896B, BPFP=0.3484 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 545,424B, BPFP=0.3462 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,133,116B, BPFP=0.7192 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,130,052B, BPFP=0.7173 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,407,584B, BPFP=0.8935 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,398,640B, BPFP=0.8878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 737,668B, BPFP=0.4682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 741,284B, BPFP=0.4705 +⌛️ [2/4] FRONTEND: Frontend time: 33.363s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.728s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14541288 12.97440943 + layer.9.1 0.14479464 0.67614623 + layer.19.0 0.11855170 37.55074697 + layer.19.1 0.11778439 30.00965835 + layer.29.0 0.12648388 168.89003087 + layer.29.1 0.12520221 132.07311302 + layer.39.0 8.37129624 1645.82824179 + layer.39.1 8.45478741 1794.30792980 + ------------------------------------------------------------------------------------- + TOTAL 2.20053917 477.78878456 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7642664 +BPFP 0.6064 bits/point +EBPFP 0.6064 equivalent bits/point +MSE 477.788785 +---------------------- ---------------------------------------------------------- +Time: 66.319s Load: 1.228s, Pack+Encode: 33.363s, Decode+Unpack: 31.728s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 477.7888 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 540,164B, BPFP=0.3429 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 539,880B, BPFP=0.3427 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,168,816B, BPFP=0.7419 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,163,508B, BPFP=0.7385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,514,284B, BPFP=0.9612 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,526,348B, BPFP=0.9688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 909,376B, BPFP=0.5772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 916,644B, BPFP=0.5818 +⌛️ [2/4] FRONTEND: Frontend time: 33.491s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.877s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14458197 3.02914612 + layer.9.1 0.14461228 4.28227925 + layer.19.0 0.12127609 15.07185875 + layer.19.1 0.12505172 10.89190466 + layer.29.0 0.11568762 493.60992850 + layer.29.1 0.11796058 236.47101479 + layer.39.0 8.63782956 2562.00812480 + layer.39.1 8.69862780 2706.99707507 + ------------------------------------------------------------------------------------- + TOTAL 2.26320345 754.04516649 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8279020 +BPFP 0.6569 bits/point +EBPFP 0.6569 equivalent bits/point +MSE 754.045166 +---------------------- ---------------------------------------------------------- +Time: 66.596s Load: 1.227s, Pack+Encode: 33.491s, Decode+Unpack: 31.877s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 754.0452 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 543,124B, BPFP=0.3447 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 543,760B, BPFP=0.3452 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,199,284B, BPFP=0.7612 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,193,752B, BPFP=0.7577 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,557,844B, BPFP=0.9888 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,576,280B, BPFP=1.0005 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 961,084B, BPFP=0.6100 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 945,360B, BPFP=0.6001 +⌛️ [2/4] FRONTEND: Frontend time: 33.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.804s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14478087 8.44341015 + layer.9.1 0.14472154 4.22631844 + layer.19.0 0.13423899 11.36134272 + layer.19.1 0.13534726 10.07335739 + layer.29.0 0.11251127 383.55760481 + layer.29.1 0.11242151 307.61573773 + layer.39.0 10.58490794 2695.07604810 + layer.39.1 8.80008176 3441.54306142 + ------------------------------------------------------------------------------------- + TOTAL 2.52112639 857.73711010 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8520488 +BPFP 0.6760 bits/point +EBPFP 0.6760 equivalent bits/point +MSE 857.737110 +---------------------- ---------------------------------------------------------- +Time: 66.654s Load: 1.223s, Pack+Encode: 33.627s, Decode+Unpack: 31.804s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 857.7371 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.234s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 513,596B, BPFP=0.3260 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 522,688B, BPFP=0.3318 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,050,736B, BPFP=0.6670 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,038,912B, BPFP=0.6594 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,341,360B, BPFP=0.8514 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,327,112B, BPFP=0.8424 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 867,124B, BPFP=0.5504 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 852,648B, BPFP=0.5412 +⌛️ [2/4] FRONTEND: Frontend time: 33.446s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.849s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14590075 7.21889091 + layer.9.1 0.14620647 7.09912760 + layer.19.0 0.11628058 15.69682574 + layer.19.1 0.11601873 13.87662496 + layer.29.0 0.11558260 76.91199322 + layer.29.1 0.11828149 159.46719613 + layer.39.0 28.43028163 3188.75105622 + layer.39.1 24.81181701 3408.39096523 + ------------------------------------------------------------------------------------- + TOTAL 6.75004616 859.67658500 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7514176 +BPFP 0.5962 bits/point +EBPFP 0.5962 equivalent bits/point +MSE 859.676585 +---------------------- ---------------------------------------------------------- +Time: 66.529s Load: 1.234s, Pack+Encode: 33.446s, Decode+Unpack: 31.849s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 859.6766 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.261s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 467,520B, BPFP=0.2968 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 470,012B, BPFP=0.2983 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,069,820B, BPFP=0.6791 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,064,328B, BPFP=0.6756 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,372,532B, BPFP=0.8712 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,369,484B, BPFP=0.8693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 894,916B, BPFP=0.5680 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 889,088B, BPFP=0.5643 +⌛️ [2/4] FRONTEND: Frontend time: 33.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.742s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11529700 0.38552661 + layer.9.1 0.14629077 0.37929908 + layer.19.0 0.09721754 10.17462220 + layer.19.1 0.12446257 13.58557188 + layer.29.0 4.28687864 268.95439958 + layer.29.1 4.28715508 246.16728957 + layer.39.0 11.34089363 2047.24699383 + layer.39.1 19.75513766 2280.01641209 + ------------------------------------------------------------------------------------- + TOTAL 5.01916661 608.36376435 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7597700 +BPFP 0.6028 bits/point +EBPFP 0.6028 equivalent bits/point +MSE 608.363764 +---------------------- ---------------------------------------------------------- +Time: 66.648s Load: 1.261s, Pack+Encode: 33.646s, Decode+Unpack: 31.742s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 608.3638 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.233s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 468,748B, BPFP=0.2975 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 474,128B, BPFP=0.3010 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,011,840B, BPFP=0.6423 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,033,308B, BPFP=0.6559 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,284,144B, BPFP=0.8151 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,303,980B, BPFP=0.8277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 900,116B, BPFP=0.5713 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 949,352B, BPFP=0.6026 +⌛️ [2/4] FRONTEND: Frontend time: 33.280s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.811s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14538718 4.37095823 + layer.9.1 0.14538559 4.34887338 + layer.19.0 0.11434236 18.03938749 + layer.19.1 0.11406084 9.00136535 + layer.29.0 0.11219077 280.68575317 + layer.29.1 0.11281304 334.22160383 + layer.39.0 79.88316542 2884.22554436 + layer.39.1 46.71980622 2877.59733507 + ------------------------------------------------------------------------------------- + TOTAL 15.91839393 801.56135261 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7425616 +BPFP 0.5892 bits/point +EBPFP 0.5892 equivalent bits/point +MSE 801.561353 +---------------------- ---------------------------------------------------------- +Time: 66.324s Load: 1.233s, Pack+Encode: 33.280s, Decode+Unpack: 31.811s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 801.5614 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 523,704B, BPFP=0.3324 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 517,052B, BPFP=0.3282 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,039,936B, BPFP=0.6601 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,056,236B, BPFP=0.6704 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,332,832B, BPFP=0.8460 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,336,696B, BPFP=0.8485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 933,560B, BPFP=0.5926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 934,400B, BPFP=0.5931 +⌛️ [2/4] FRONTEND: Frontend time: 33.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.789s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14512126 3.29176273 + layer.9.1 0.14517278 3.17536105 + layer.19.0 0.11689420 22.66456268 + layer.19.1 0.12099910 33.57851601 + layer.29.0 0.11847120 245.13946214 + layer.29.1 0.12399357 75.32275756 + layer.39.0 75.86630139 3666.43906402 + layer.39.1 56.61936342 3773.15242119 + ------------------------------------------------------------------------------------- + TOTAL 16.65703962 977.84548842 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7674416 +BPFP 0.6089 bits/point +EBPFP 0.6089 equivalent bits/point +MSE 977.845488 +---------------------- ---------------------------------------------------------- +Time: 66.672s Load: 1.227s, Pack+Encode: 33.656s, Decode+Unpack: 31.789s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 977.8455 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.226s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 468,640B, BPFP=0.2975 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 466,984B, BPFP=0.2964 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,022,044B, BPFP=0.6487 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,016,060B, BPFP=0.6449 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,282,732B, BPFP=0.8142 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,281,976B, BPFP=0.8137 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 959,508B, BPFP=0.6090 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 953,152B, BPFP=0.6050 +⌛️ [2/4] FRONTEND: Frontend time: 33.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.802s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488649 0.36896710 + layer.9.1 0.14606862 4.31881798 + layer.19.0 0.08767178 9.38667495 + layer.19.1 0.11443626 7.94650583 + layer.29.0 0.10933029 95.83020190 + layer.29.1 0.10817130 228.00452957 + layer.39.0 52.66717785 2789.56483588 + layer.39.1 62.91127214 3662.57556061 + ------------------------------------------------------------------------------------- + TOTAL 14.53237684 849.74951173 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7451096 +BPFP 0.5912 bits/point +EBPFP 0.5912 equivalent bits/point +MSE 849.749512 +---------------------- ---------------------------------------------------------- +Time: 66.642s Load: 1.226s, Pack+Encode: 33.614s, Decode+Unpack: 31.802s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 849.7495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 477,452B, BPFP=0.3031 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 481,348B, BPFP=0.3055 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 999,880B, BPFP=0.6347 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,001,868B, BPFP=0.6359 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,246,820B, BPFP=0.7914 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,245,840B, BPFP=0.7908 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 900,812B, BPFP=0.5718 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 940,948B, BPFP=0.5973 +⌛️ [2/4] FRONTEND: Frontend time: 33.480s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.768s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509552 4.49276512 + layer.9.1 0.14520687 4.57167340 + layer.19.0 0.12118574 18.39753616 + layer.19.1 0.11709642 22.83532560 + layer.29.0 0.10963326 424.95986350 + layer.29.1 0.10842036 246.51850422 + layer.39.0 53.79489966 2470.50146246 + layer.39.1 62.27410526 3173.07539812 + ------------------------------------------------------------------------------------- + TOTAL 14.60195539 795.66906607 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7294968 +BPFP 0.5788 bits/point +EBPFP 0.5788 equivalent bits/point +MSE 795.669066 +---------------------- ---------------------------------------------------------- +Time: 66.472s Load: 1.225s, Pack+Encode: 33.480s, Decode+Unpack: 31.768s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 795.6691 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.229s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 571,372B, BPFP=0.3627 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 568,468B, BPFP=0.3608 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,102,004B, BPFP=0.6995 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,099,960B, BPFP=0.6982 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,331,084B, BPFP=0.8449 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,327,364B, BPFP=0.8425 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 927,512B, BPFP=0.5887 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 940,604B, BPFP=0.5970 +⌛️ [2/4] FRONTEND: Frontend time: 33.359s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.769s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14577075 8.41660531 + layer.9.1 0.14541274 4.62460804 + layer.19.0 0.13069581 10.93354297 + layer.19.1 0.13545482 8.95734799 + layer.29.0 0.11331055 129.03973026 + layer.29.1 0.11244963 214.87408596 + layer.39.0 32.27446072 3020.56678583 + layer.39.1 16.59366367 3119.74520637 + ------------------------------------------------------------------------------------- + TOTAL 6.20640234 814.64473909 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7868368 +BPFP 0.6243 bits/point +EBPFP 0.6243 equivalent bits/point +MSE 814.644739 +---------------------- ---------------------------------------------------------- +Time: 66.357s Load: 1.229s, Pack+Encode: 33.359s, Decode+Unpack: 31.769s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 814.6447 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.230s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 449,240B, BPFP=0.2852 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 446,276B, BPFP=0.2833 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 961,316B, BPFP=0.6102 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 971,036B, BPFP=0.6164 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,248,520B, BPFP=0.7925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,253,196B, BPFP=0.7955 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 856,456B, BPFP=0.5436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 894,264B, BPFP=0.5676 +⌛️ [2/4] FRONTEND: Frontend time: 33.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.842s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585920 3.06120511 + layer.9.1 0.14576220 4.30370999 + layer.19.0 0.12270736 23.44099620 + layer.19.1 0.12453605 30.16325002 + layer.29.0 0.11393550 257.24234238 + layer.29.1 0.11678154 334.13645596 + layer.39.0 53.83016636 3212.93305167 + layer.39.1 40.65720720 3959.68248294 + ------------------------------------------------------------------------------------- + TOTAL 11.90711942 978.12043678 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7080304 +BPFP 0.5618 bits/point +EBPFP 0.5618 equivalent bits/point +MSE 978.120437 +---------------------- ---------------------------------------------------------- +Time: 66.633s Load: 1.230s, Pack+Encode: 33.561s, Decode+Unpack: 31.842s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.1204 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.235s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 458,120B, BPFP=0.2908 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 457,596B, BPFP=0.2905 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,041,084B, BPFP=0.6608 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,046,620B, BPFP=0.6643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,303,940B, BPFP=0.8277 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,309,968B, BPFP=0.8315 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 822,484B, BPFP=0.5221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 839,132B, BPFP=0.5326 +⌛️ [2/4] FRONTEND: Frontend time: 33.479s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.746s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568599 4.41917192 + layer.9.1 0.03329684 4.31072810 + layer.19.0 0.11848472 11.54732821 + layer.19.1 0.11973745 7.42448001 + layer.29.0 0.10886538 77.27422713 + layer.29.1 0.10946879 35.82877498 + layer.39.0 14.08931437 2590.49870003 + layer.39.1 9.95616799 2179.33636659 + ------------------------------------------------------------------------------------- + TOTAL 3.08512769 613.82997212 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7278944 +BPFP 0.5775 bits/point +EBPFP 0.5775 equivalent bits/point +MSE 613.829972 +---------------------- ---------------------------------------------------------- +Time: 66.459s Load: 1.235s, Pack+Encode: 33.479s, Decode+Unpack: 31.746s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 613.8300 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.271s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 463,932B, BPFP=0.2945 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 461,980B, BPFP=0.2932 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,000,284B, BPFP=0.6349 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,009,008B, BPFP=0.6405 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,278,220B, BPFP=0.8113 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,284,136B, BPFP=0.8151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 844,392B, BPFP=0.5360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 846,728B, BPFP=0.5375 +⌛️ [2/4] FRONTEND: Frontend time: 33.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.791s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14493624 0.51045238 + layer.9.1 0.14482686 3.16797415 + layer.19.0 0.11946148 16.69940917 + layer.19.1 0.12828579 12.26114240 + layer.29.0 0.10467725 78.88408454 + layer.29.1 0.10613328 72.24842074 + layer.39.0 22.00188902 3049.52258694 + layer.39.1 19.26198661 3370.38381540 + ------------------------------------------------------------------------------------- + TOTAL 5.25152457 825.45973572 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7188680 +BPFP 0.5704 bits/point +EBPFP 0.5704 equivalent bits/point +MSE 825.459736 +---------------------- ---------------------------------------------------------- +Time: 66.696s Load: 1.271s, Pack+Encode: 33.634s, Decode+Unpack: 31.791s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 825.4597 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 460,196B, BPFP=0.2921 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 462,952B, BPFP=0.2939 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,011,668B, BPFP=0.6422 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 988,796B, BPFP=0.6276 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,287,800B, BPFP=0.8174 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,251,004B, BPFP=0.7941 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 823,828B, BPFP=0.5229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 828,356B, BPFP=0.5258 +⌛️ [2/4] FRONTEND: Frontend time: 33.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.768s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14475438 2.99697161 + layer.9.1 0.14492096 4.33146093 + layer.19.0 0.11744098 16.90116946 + layer.19.1 0.11578254 23.25873162 + layer.29.0 0.11402616 57.59306955 + layer.29.1 0.11062706 49.96605866 + layer.39.0 28.92800668 3740.90315242 + layer.39.1 10.80449708 3372.31654209 + ------------------------------------------------------------------------------------- + TOTAL 5.06000698 908.53339454 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7114600 +BPFP 0.5645 bits/point +EBPFP 0.5645 equivalent bits/point +MSE 908.533395 +---------------------- ---------------------------------------------------------- +Time: 66.581s Load: 1.208s, Pack+Encode: 33.606s, Decode+Unpack: 31.768s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 908.5334 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.212s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 387,720B, BPFP=0.2461 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 402,624B, BPFP=0.2556 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 902,944B, BPFP=0.5731 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 894,232B, BPFP=0.5676 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,096,220B, BPFP=0.6958 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,077,896B, BPFP=0.6842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 716,780B, BPFP=0.4550 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 730,392B, BPFP=0.4636 +⌛️ [2/4] FRONTEND: Frontend time: 33.427s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.806s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14546214 3.04382281 + layer.9.1 0.14553630 3.00922164 + layer.19.0 0.04765745 20.47874477 + layer.19.1 0.04191649 31.91560621 + layer.29.0 0.16505912 80.91029209 + layer.29.1 0.15755973 47.55205456 + layer.39.0 42.51041751 1522.68817030 + layer.39.1 31.38856333 1470.78826779 + ------------------------------------------------------------------------------------- + TOTAL 9.32527151 397.54827252 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6208808 +BPFP 0.4926 bits/point +EBPFP 0.4926 equivalent bits/point +MSE 397.548273 +---------------------- ---------------------------------------------------------- +Time: 66.445s Load: 1.212s, Pack+Encode: 33.427s, Decode+Unpack: 31.806s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 397.5483 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.224s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,648B, BPFP=0.2734 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 430,596B, BPFP=0.2733 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 956,548B, BPFP=0.6072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 966,928B, BPFP=0.6138 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,184,896B, BPFP=0.7521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,180,552B, BPFP=0.7494 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 809,424B, BPFP=0.5138 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 823,524B, BPFP=0.5227 +⌛️ [2/4] FRONTEND: Frontend time: 33.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.732s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03343166 4.37125783 + layer.9.1 0.03311388 3.13178547 + layer.19.0 0.03842411 17.81761608 + layer.19.1 0.03806642 15.52699972 + layer.29.0 4.26870163 43.89481435 + layer.29.1 4.26552788 35.36006053 + layer.39.0 33.95300821 3153.92817680 + layer.39.1 48.19954501 2315.41533962 + ------------------------------------------------------------------------------------- + TOTAL 11.35372735 698.68075630 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6783116 +BPFP 0.5382 bits/point +EBPFP 0.5382 equivalent bits/point +MSE 698.680756 +---------------------- ---------------------------------------------------------- +Time: 66.593s Load: 1.224s, Pack+Encode: 33.637s, Decode+Unpack: 31.732s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 698.6808 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.217s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 457,120B, BPFP=0.2902 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 459,168B, BPFP=0.2915 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 955,240B, BPFP=0.6063 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 965,920B, BPFP=0.6131 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,153,124B, BPFP=0.7319 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,165,112B, BPFP=0.7396 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 790,972B, BPFP=0.5021 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 841,588B, BPFP=0.5342 +⌛️ [2/4] FRONTEND: Frontend time: 33.679s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.719s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549638 0.36393873 + layer.9.1 0.14520178 0.39062099 + layer.19.0 0.11487435 18.32714495 + layer.19.1 0.11481158 21.39573347 + layer.29.0 0.10827909 263.51905265 + layer.29.1 0.10618535 175.74073773 + layer.39.0 9.83978281 2013.08336042 + layer.39.1 9.67554703 2841.38674033 + ------------------------------------------------------------------------------------- + TOTAL 2.53127230 666.77591616 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6788244 +BPFP 0.5386 bits/point +EBPFP 0.5386 equivalent bits/point +MSE 666.775916 +---------------------- ---------------------------------------------------------- +Time: 66.615s Load: 1.217s, Pack+Encode: 33.679s, Decode+Unpack: 31.719s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 666.7759 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.217s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 414,448B, BPFP=0.2631 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 406,820B, BPFP=0.2582 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 942,600B, BPFP=0.5983 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 939,172B, BPFP=0.5961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,195,376B, BPFP=0.7588 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,188,640B, BPFP=0.7545 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 746,892B, BPFP=0.4741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 778,924B, BPFP=0.4944 +⌛️ [2/4] FRONTEND: Frontend time: 33.377s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.677s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092318 0.35329878 + layer.9.1 0.00095285 4.33722572 + layer.19.0 0.08568402 16.44927461 + layer.19.1 0.08404610 16.85223153 + layer.29.0 0.12100375 68.22856577 + layer.29.1 0.12795564 68.36319873 + layer.39.0 12.85620633 3281.64900877 + layer.39.1 12.98640239 3712.86772831 + ------------------------------------------------------------------------------------- + TOTAL 3.28289678 896.13756653 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6612872 +BPFP 0.5247 bits/point +EBPFP 0.5247 equivalent bits/point +MSE 896.137567 +---------------------- ---------------------------------------------------------- +Time: 66.271s Load: 1.217s, Pack+Encode: 33.377s, Decode+Unpack: 31.677s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 896.1376 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,688B, BPFP=0.2620 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 413,736B, BPFP=0.2626 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 951,332B, BPFP=0.6039 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 939,508B, BPFP=0.5964 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,204,024B, BPFP=0.7643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,186,788B, BPFP=0.7533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 788,992B, BPFP=0.5008 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 827,828B, BPFP=0.5255 +⌛️ [2/4] FRONTEND: Frontend time: 33.396s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.736s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00113376 4.28923642 + layer.9.1 0.00100095 0.39239345 + layer.19.0 0.00983371 16.75478855 + layer.19.1 0.00806405 10.31857011 + layer.29.0 4.28365570 79.43084782 + layer.29.1 4.28597952 44.53752133 + layer.39.0 8.41906814 1906.78680533 + layer.39.1 8.59662605 2735.89632759 + ------------------------------------------------------------------------------------- + TOTAL 3.20067024 599.80081133 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6724896 +BPFP 0.5336 bits/point +EBPFP 0.5336 equivalent bits/point +MSE 599.800811 +---------------------- ---------------------------------------------------------- +Time: 66.340s Load: 1.209s, Pack+Encode: 33.396s, Decode+Unpack: 31.736s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 599.8008 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.220s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 465,340B, BPFP=0.2954 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 472,628B, BPFP=0.3000 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 994,896B, BPFP=0.6315 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,001,948B, BPFP=0.6360 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,231,556B, BPFP=0.7817 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,237,876B, BPFP=0.7857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 805,972B, BPFP=0.5116 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 811,652B, BPFP=0.5152 +⌛️ [2/4] FRONTEND: Frontend time: 33.329s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.777s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550294 4.37612795 + layer.9.1 0.14526658 0.36562395 + layer.19.0 0.11599200 10.15316766 + layer.19.1 0.11361485 13.98874081 + layer.29.0 4.26439454 85.93471726 + layer.29.1 4.25587461 49.01366997 + layer.39.0 8.37236706 1890.91810205 + layer.39.1 8.35116642 1719.58108547 + ------------------------------------------------------------------------------------- + TOTAL 3.22052237 471.79140439 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7021868 +BPFP 0.5571 bits/point +EBPFP 0.5571 equivalent bits/point +MSE 471.791404 +---------------------- ---------------------------------------------------------- +Time: 66.326s Load: 1.220s, Pack+Encode: 33.329s, Decode+Unpack: 31.777s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 471.7914 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 424,108B, BPFP=0.2692 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 430,024B, BPFP=0.2730 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,012,240B, BPFP=0.6425 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,023,068B, BPFP=0.6494 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,244,140B, BPFP=0.7897 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,266,716B, BPFP=0.8040 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 801,536B, BPFP=0.5088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 840,280B, BPFP=0.5334 +⌛️ [2/4] FRONTEND: Frontend time: 33.443s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.793s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00080404 4.34177720 + layer.9.1 0.00082438 3.06909315 + layer.19.0 0.00843097 10.50982339 + layer.19.1 0.00674472 11.86299942 + layer.29.0 4.27713270 36.17189277 + layer.29.1 4.27133426 43.94344126 + layer.39.0 22.97048921 2021.55492363 + layer.39.1 18.06488920 3006.77153071 + ------------------------------------------------------------------------------------- + TOTAL 6.20008118 642.27818519 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7042112 +BPFP 0.5587 bits/point +EBPFP 0.5587 equivalent bits/point +MSE 642.278185 +---------------------- ---------------------------------------------------------- +Time: 66.463s Load: 1.227s, Pack+Encode: 33.443s, Decode+Unpack: 31.793s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 642.2782 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.271s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 413,812B, BPFP=0.2627 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 420,248B, BPFP=0.2668 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,006,260B, BPFP=0.6387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,000,832B, BPFP=0.6353 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,253,332B, BPFP=0.7956 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,245,356B, BPFP=0.7905 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 830,608B, BPFP=0.5272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 823,392B, BPFP=0.5226 +⌛️ [2/4] FRONTEND: Frontend time: 33.475s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.772s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14506060 8.33978980 + layer.9.1 0.14523201 3.00316772 + layer.19.0 0.04621643 7.79020251 + layer.19.1 0.04629335 15.45878697 + layer.29.0 4.27940669 54.30730013 + layer.29.1 4.27759670 103.82547936 + layer.39.0 19.91382637 1856.45043874 + layer.39.1 24.01088215 2025.26649334 + ------------------------------------------------------------------------------------- + TOTAL 6.60806429 509.30520732 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6993840 +BPFP 0.5549 bits/point +EBPFP 0.5549 equivalent bits/point +MSE 509.305207 +---------------------- ---------------------------------------------------------- +Time: 66.518s Load: 1.271s, Pack+Encode: 33.475s, Decode+Unpack: 31.772s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 509.3052 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.217s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 384,180B, BPFP=0.2439 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 376,892B, BPFP=0.2392 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 791,072B, BPFP=0.5021 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 807,136B, BPFP=0.5123 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 965,652B, BPFP=0.6129 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 972,120B, BPFP=0.6171 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 671,388B, BPFP=0.4262 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 689,612B, BPFP=0.4377 +⌛️ [2/4] FRONTEND: Frontend time: 33.246s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.723s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66985745 3.21631923 + layer.9.1 2.66884121 0.38782952 + layer.19.0 3.21935619 15.24828871 + layer.19.1 3.21606501 22.89566491 + layer.29.0 4.24164606 63.11840368 + layer.29.1 4.23648681 60.88925394 + layer.39.0 8.06392628 1284.19637634 + layer.39.1 8.17747540 1466.37455314 + ------------------------------------------------------------------------------------- + TOTAL 4.56170680 364.54083618 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5658052 +BPFP 0.4489 bits/point +EBPFP 0.4489 equivalent bits/point +MSE 364.540836 +---------------------- ---------------------------------------------------------- +Time: 66.187s Load: 1.217s, Pack+Encode: 33.246s, Decode+Unpack: 31.723s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 364.5408 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.236s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 370,732B, BPFP=0.2353 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 367,848B, BPFP=0.2335 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 849,252B, BPFP=0.5391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 818,720B, BPFP=0.5197 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,092,584B, BPFP=0.6935 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,054,412B, BPFP=0.6693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 733,684B, BPFP=0.4657 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 726,744B, BPFP=0.4613 +⌛️ [2/4] FRONTEND: Frontend time: 33.332s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.722s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66795352 4.30289560 + layer.9.1 2.66862889 4.41324431 + layer.19.0 3.22250645 15.40129387 + layer.19.1 3.22577319 13.43393778 + layer.29.0 4.25792136 38.20320117 + layer.29.1 4.25014663 61.08448265 + layer.39.0 8.65209937 1577.55021124 + layer.39.1 8.58450170 1435.65940851 + ------------------------------------------------------------------------------------- + TOTAL 4.69119139 393.75608439 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6013976 +BPFP 0.4772 bits/point +EBPFP 0.4772 equivalent bits/point +MSE 393.756084 +---------------------- ---------------------------------------------------------- +Time: 66.290s Load: 1.236s, Pack+Encode: 33.332s, Decode+Unpack: 31.722s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 393.7561 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.219s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,092B, BPFP=0.2730 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 427,696B, BPFP=0.2715 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 998,044B, BPFP=0.6335 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,002,424B, BPFP=0.6363 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,257,800B, BPFP=0.7984 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,260,692B, BPFP=0.8002 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 782,796B, BPFP=0.4969 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 764,376B, BPFP=0.4852 +⌛️ [2/4] FRONTEND: Frontend time: 33.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.858s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092775 4.43565732 + layer.9.1 0.00093166 4.44606563 + layer.19.0 0.08227225 15.79723046 + layer.19.1 0.08381199 12.80239351 + layer.29.0 0.10725604 43.06012350 + layer.29.1 0.10756977 33.16285140 + layer.39.0 7.96294394 1894.30354241 + layer.39.1 7.95922050 1571.85619110 + ------------------------------------------------------------------------------------- + TOTAL 2.03811674 447.48300692 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6923920 +BPFP 0.5494 bits/point +EBPFP 0.5494 equivalent bits/point +MSE 447.483007 +---------------------- ---------------------------------------------------------- +Time: 66.630s Load: 1.219s, Pack+Encode: 33.553s, Decode+Unpack: 31.858s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 447.4830 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.217s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 387,760B, BPFP=0.2461 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 379,824B, BPFP=0.2411 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 872,836B, BPFP=0.5540 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 874,664B, BPFP=0.5552 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,059,308B, BPFP=0.6724 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,067,876B, BPFP=0.6778 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 702,908B, BPFP=0.4462 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 764,228B, BPFP=0.4851 +⌛️ [2/4] FRONTEND: Frontend time: 33.251s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.763s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66308118 0.37508764 + layer.9.1 2.66351027 3.16668941 + layer.19.0 3.21594155 25.96167127 + layer.19.1 3.21498593 20.57711829 + layer.29.0 4.33566519 42.85122989 + layer.29.1 4.34101296 55.32923200 + layer.39.0 8.65310735 1368.63357166 + layer.39.1 8.66575030 2593.29525512 + ------------------------------------------------------------------------------------- + TOTAL 4.71913184 513.77373191 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6109404 +BPFP 0.4847 bits/point +EBPFP 0.4847 equivalent bits/point +MSE 513.773732 +---------------------- ---------------------------------------------------------- +Time: 66.232s Load: 1.217s, Pack+Encode: 33.251s, Decode+Unpack: 31.763s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 513.7737 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,152B, BPFP=0.2578 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 410,224B, BPFP=0.2604 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 903,344B, BPFP=0.5734 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 896,220B, BPFP=0.5689 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,081,064B, BPFP=0.6862 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,079,208B, BPFP=0.6850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,356B, BPFP=0.4382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 726,080B, BPFP=0.4609 +⌛️ [2/4] FRONTEND: Frontend time: 33.458s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.707s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.65959259 4.51253916 + layer.9.1 2.65993726 4.45164445 + layer.19.0 3.20866700 15.43412567 + layer.19.1 3.21007805 13.48612183 + layer.29.0 4.27255361 32.66085321 + layer.29.1 4.27602442 37.38435367 + layer.39.0 19.11658068 1983.07312317 + layer.39.1 9.60360322 2333.48261293 + ------------------------------------------------------------------------------------- + TOTAL 6.12587960 553.06067176 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6192648 +BPFP 0.4913 bits/point +EBPFP 0.4913 equivalent bits/point +MSE 553.060672 +---------------------- ---------------------------------------------------------- +Time: 66.386s Load: 1.221s, Pack+Encode: 33.458s, Decode+Unpack: 31.707s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 553.0607 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.224s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 416,436B, BPFP=0.2643 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 403,904B, BPFP=0.2564 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 921,948B, BPFP=0.5852 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 911,536B, BPFP=0.5786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,118,128B, BPFP=0.7097 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,103,508B, BPFP=0.7005 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 731,056B, BPFP=0.4640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 733,540B, BPFP=0.4656 +⌛️ [2/4] FRONTEND: Frontend time: 33.378s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.754s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67078767 4.40246530 + layer.9.1 2.67131261 4.44757507 + layer.19.0 3.30595795 11.81913948 + layer.19.1 3.30543206 12.06962697 + layer.29.0 0.11228124 31.86496588 + layer.29.1 0.11507649 37.31060083 + layer.39.0 11.41791162 1707.15648359 + layer.39.1 11.38150745 2835.87650309 + ------------------------------------------------------------------------------------- + TOTAL 4.37253339 580.61842003 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6340056 +BPFP 0.5030 bits/point +EBPFP 0.5030 equivalent bits/point +MSE 580.618420 +---------------------- ---------------------------------------------------------- +Time: 66.356s Load: 1.224s, Pack+Encode: 33.378s, Decode+Unpack: 31.754s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 580.6184 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 428,464B, BPFP=0.2720 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 426,544B, BPFP=0.2707 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 942,352B, BPFP=0.5982 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 943,216B, BPFP=0.5987 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,202,660B, BPFP=0.7634 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,188,596B, BPFP=0.7545 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 781,500B, BPFP=0.4961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 786,096B, BPFP=0.4990 +⌛️ [2/4] FRONTEND: Frontend time: 33.483s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.792s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14463971 2.98387926 + layer.9.1 0.14470460 0.34899216 + layer.19.0 0.12255537 24.47742322 + layer.19.1 0.11825690 23.43489245 + layer.29.0 0.11949990 119.62215632 + layer.29.1 0.11467140 72.51562500 + layer.39.0 10.68243977 2297.04029899 + layer.39.1 10.40156301 1985.31394215 + ------------------------------------------------------------------------------------- + TOTAL 2.73104133 565.71715119 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6699428 +BPFP 0.5316 bits/point +EBPFP 0.5316 equivalent bits/point +MSE 565.717151 +---------------------- ---------------------------------------------------------- +Time: 66.502s Load: 1.227s, Pack+Encode: 33.483s, Decode+Unpack: 31.792s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.7172 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.226s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 439,504B, BPFP=0.2790 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 442,164B, BPFP=0.2807 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 969,224B, BPFP=0.6152 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 970,840B, BPFP=0.6162 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,195,020B, BPFP=0.7585 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,201,688B, BPFP=0.7628 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 789,332B, BPFP=0.5010 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 810,252B, BPFP=0.5143 +⌛️ [2/4] FRONTEND: Frontend time: 33.435s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.749s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14485273 0.37256593 + layer.9.1 0.14484227 0.37584529 + layer.19.0 0.11969613 25.68282824 + layer.19.1 0.11916645 16.52315694 + layer.29.0 0.11480527 130.75822636 + layer.29.1 0.11451660 51.43235599 + layer.39.0 11.00270276 1932.01673708 + layer.39.1 11.01557422 2555.50032499 + ------------------------------------------------------------------------------------- + TOTAL 2.84701956 589.08275510 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6818024 +BPFP 0.5410 bits/point +EBPFP 0.5410 equivalent bits/point +MSE 589.082755 +---------------------- ---------------------------------------------------------- +Time: 66.410s Load: 1.226s, Pack+Encode: 33.435s, Decode+Unpack: 31.749s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 589.0828 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.226s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 411,776B, BPFP=0.2614 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 412,048B, BPFP=0.2615 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 887,276B, BPFP=0.5632 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 889,792B, BPFP=0.5648 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,060,976B, BPFP=0.6735 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,066,908B, BPFP=0.6772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 763,012B, BPFP=0.4843 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 769,092B, BPFP=0.4882 +⌛️ [2/4] FRONTEND: Frontend time: 33.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.749s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14481488 4.30416320 + layer.9.1 0.14470567 0.36240359 + layer.19.0 0.03819180 43.08340104 + layer.19.1 0.04002141 24.88292422 + layer.29.0 0.11241068 260.73661440 + layer.29.1 0.11133552 158.69883409 + layer.39.0 31.78807483 2558.70994475 + layer.39.1 43.50691623 2536.50698733 + ------------------------------------------------------------------------------------- + TOTAL 9.48580888 698.41065908 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6260880 +BPFP 0.4968 bits/point +EBPFP 0.4968 equivalent bits/point +MSE 698.410659 +---------------------- ---------------------------------------------------------- +Time: 66.533s Load: 1.226s, Pack+Encode: 33.558s, Decode+Unpack: 31.749s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 698.4107 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.216s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 434,140B, BPFP=0.2756 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 434,664B, BPFP=0.2759 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 928,812B, BPFP=0.5896 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 950,000B, BPFP=0.6030 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,176,072B, BPFP=0.7465 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,188,908B, BPFP=0.7547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 839,144B, BPFP=0.5326 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 799,200B, BPFP=0.5073 +⌛️ [2/4] FRONTEND: Frontend time: 33.499s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.812s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14528272 4.35747773 + layer.9.1 0.14516892 0.35905822 + layer.19.0 0.11319376 16.08396216 + layer.19.1 0.11666145 10.26412255 + layer.29.0 0.21118872 132.74809067 + layer.29.1 0.20646930 151.09201332 + layer.39.0 14.37750853 3769.94930127 + layer.39.1 21.76644002 2945.38609035 + ------------------------------------------------------------------------------------- + TOTAL 4.63523918 878.78001453 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6750940 +BPFP 0.5356 bits/point +EBPFP 0.5356 equivalent bits/point +MSE 878.780015 +---------------------- ---------------------------------------------------------- +Time: 66.527s Load: 1.216s, Pack+Encode: 33.499s, Decode+Unpack: 31.812s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 878.7800 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 405,520B, BPFP=0.2574 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 409,148B, BPFP=0.2597 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 918,556B, BPFP=0.5831 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 911,580B, BPFP=0.5786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,122,040B, BPFP=0.7122 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,109,076B, BPFP=0.7040 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 782,940B, BPFP=0.4970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 798,600B, BPFP=0.5069 +⌛️ [2/4] FRONTEND: Frontend time: 33.402s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.667s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14454765 0.48381769 + layer.9.1 0.14475082 0.38291192 + layer.19.0 0.04087094 21.08690994 + layer.19.1 0.11687931 18.62093506 + layer.29.0 0.10817139 112.81266250 + layer.29.1 0.10802081 127.64217379 + layer.39.0 19.80422286 1867.37764056 + layer.39.1 34.29222355 1951.73188170 + ------------------------------------------------------------------------------------- + TOTAL 6.84496092 512.51736664 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6457460 +BPFP 0.5124 bits/point +EBPFP 0.5124 equivalent bits/point +MSE 512.517367 +---------------------- ---------------------------------------------------------- +Time: 66.290s Load: 1.221s, Pack+Encode: 33.402s, Decode+Unpack: 31.667s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 512.5174 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 386,528B, BPFP=0.2453 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 390,940B, BPFP=0.2481 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 870,336B, BPFP=0.5524 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 905,364B, BPFP=0.5747 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,044,852B, BPFP=0.6632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,069,920B, BPFP=0.6791 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 739,228B, BPFP=0.4692 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 774,804B, BPFP=0.4918 +⌛️ [2/4] FRONTEND: Frontend time: 33.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.667s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14462205 0.40958422 + layer.9.1 0.14495783 0.34982860 + layer.19.0 0.04322015 20.60450647 + layer.19.1 0.03788725 20.87246354 + layer.29.0 0.10021623 37.59324728 + layer.29.1 0.10137775 47.59460818 + layer.39.0 58.66958482 1597.69922002 + layer.39.1 72.48303949 2380.04029899 + ------------------------------------------------------------------------------------- + TOTAL 16.46561320 513.14546966 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6181972 +BPFP 0.4905 bits/point +EBPFP 0.4905 equivalent bits/point +MSE 513.145470 +---------------------- ---------------------------------------------------------- +Time: 66.190s Load: 1.223s, Pack+Encode: 33.300s, Decode+Unpack: 31.667s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 513.1455 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 445,688B, BPFP=0.2829 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 446,812B, BPFP=0.2836 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,008,408B, BPFP=0.6401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,012,440B, BPFP=0.6426 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,246,020B, BPFP=0.7909 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,234,832B, BPFP=0.7838 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 778,656B, BPFP=0.4943 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 798,808B, BPFP=0.5070 +⌛️ [2/4] FRONTEND: Frontend time: 33.401s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.761s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527927 4.33757484 + layer.9.1 0.14528875 0.36351809 + layer.19.0 0.12591341 21.44296646 + layer.19.1 0.13556211 8.52235716 + layer.29.0 0.11238900 49.76126808 + layer.29.1 0.11028371 83.20641046 + layer.39.0 11.48751193 2232.59278518 + layer.39.1 11.29491489 2853.24471888 + ------------------------------------------------------------------------------------- + TOTAL 2.94464289 656.68394989 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6971664 +BPFP 0.5532 bits/point +EBPFP 0.5532 equivalent bits/point +MSE 656.683950 +---------------------- ---------------------------------------------------------- +Time: 66.385s Load: 1.223s, Pack+Encode: 33.401s, Decode+Unpack: 31.761s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 656.6839 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.222s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 434,580B, BPFP=0.2758 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 443,736B, BPFP=0.2817 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 975,080B, BPFP=0.6189 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 974,000B, BPFP=0.6182 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,199,380B, BPFP=0.7613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,195,428B, BPFP=0.7588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 774,916B, BPFP=0.4919 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 751,944B, BPFP=0.4773 +⌛️ [2/4] FRONTEND: Frontend time: 33.411s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.752s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14489184 4.23887379 + layer.9.1 0.14511764 4.33949019 + layer.19.0 0.03976490 13.52374726 + layer.19.1 0.11370806 14.37450743 + layer.29.0 0.10933599 48.02162212 + layer.29.1 0.11012027 36.14448428 + layer.39.0 9.10787636 1647.48472538 + layer.39.1 9.00026152 1496.75235619 + ------------------------------------------------------------------------------------- + TOTAL 2.34638457 408.10997583 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6749064 +BPFP 0.5355 bits/point +EBPFP 0.5355 equivalent bits/point +MSE 408.109976 +---------------------- ---------------------------------------------------------- +Time: 66.385s Load: 1.222s, Pack+Encode: 33.411s, Decode+Unpack: 31.752s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 408.1100 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.232s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 401,648B, BPFP=0.2549 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 395,396B, BPFP=0.2510 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 908,732B, BPFP=0.5768 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 901,684B, BPFP=0.5723 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,049,456B, BPFP=0.6661 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,046,896B, BPFP=0.6645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 636,364B, BPFP=0.4039 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 648,500B, BPFP=0.4116 +⌛️ [2/4] FRONTEND: Frontend time: 33.488s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.743s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083307 4.37904526 + layer.9.1 0.00247171 4.43166728 + layer.19.0 0.00642632 14.12642946 + layer.19.1 0.00641681 16.04484761 + layer.29.0 0.10256791 32.80776223 + layer.29.1 0.10162673 37.71458096 + layer.39.0 8.50517638 1187.26876828 + layer.39.1 8.55767781 1392.06954826 + ------------------------------------------------------------------------------------- + TOTAL 2.16039959 336.10533117 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5988676 +BPFP 0.4752 bits/point +EBPFP 0.4752 equivalent bits/point +MSE 336.105331 +---------------------- ---------------------------------------------------------- +Time: 66.464s Load: 1.232s, Pack+Encode: 33.488s, Decode+Unpack: 31.743s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 336.1053 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.220s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 410,936B, BPFP=0.2608 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 413,648B, BPFP=0.2626 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 912,712B, BPFP=0.5793 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 916,068B, BPFP=0.5815 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,179,744B, BPFP=0.7488 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,170,512B, BPFP=0.7430 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 792,768B, BPFP=0.5032 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 777,300B, BPFP=0.4934 +⌛️ [2/4] FRONTEND: Frontend time: 33.354s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.765s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00064391 0.36415613 + layer.9.1 0.00065402 0.34280533 + layer.19.0 0.08134466 10.67030590 + layer.19.1 0.08141702 12.42626747 + layer.29.0 0.11551180 56.79620470 + layer.29.1 0.11251285 92.11376747 + layer.39.0 10.61319619 3160.76048099 + layer.39.1 10.43102047 2369.16217095 + ------------------------------------------------------------------------------------- + TOTAL 2.67953762 712.82951987 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6573688 +BPFP 0.5216 bits/point +EBPFP 0.5216 equivalent bits/point +MSE 712.829520 +---------------------- ---------------------------------------------------------- +Time: 66.340s Load: 1.220s, Pack+Encode: 33.354s, Decode+Unpack: 31.765s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 712.8295 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.239s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 416,900B, BPFP=0.2646 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 418,684B, BPFP=0.2658 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 960,700B, BPFP=0.6098 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 973,640B, BPFP=0.6180 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,206,720B, BPFP=0.7660 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,223,424B, BPFP=0.7766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 842,248B, BPFP=0.5346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 880,064B, BPFP=0.5586 +⌛️ [2/4] FRONTEND: Frontend time: 33.442s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.840s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14441776 0.36078847 + layer.9.1 0.14449203 4.28811291 + layer.19.0 0.11315974 19.02410653 + layer.19.1 0.11435745 9.86043059 + layer.29.0 0.12811458 84.11576820 + layer.29.1 0.12952277 113.89408312 + layer.39.0 31.10682331 2604.47985050 + layer.39.1 16.99297713 2446.35212870 + ------------------------------------------------------------------------------------- + TOTAL 6.10923309 660.29690863 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6922380 +BPFP 0.5492 bits/point +EBPFP 0.5492 equivalent bits/point +MSE 660.296909 +---------------------- ---------------------------------------------------------- +Time: 66.520s Load: 1.239s, Pack+Encode: 33.442s, Decode+Unpack: 31.840s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 660.2969 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 407,200B, BPFP=0.2585 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 404,440B, BPFP=0.2567 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 945,396B, BPFP=0.6001 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 951,172B, BPFP=0.6038 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,173,364B, BPFP=0.7448 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,167,360B, BPFP=0.7410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 768,976B, BPFP=0.4881 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 780,524B, BPFP=0.4954 +⌛️ [2/4] FRONTEND: Frontend time: 33.356s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.750s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078516 4.42923937 + layer.9.1 0.00079184 3.11925393 + layer.19.0 3.22632161 17.18222777 + layer.19.1 3.22513146 15.66282347 + layer.29.0 0.10494786 36.83026792 + layer.29.1 0.10251782 31.57885877 + layer.39.0 10.88842496 1776.93841404 + layer.39.1 10.78217420 2677.35261618 + ------------------------------------------------------------------------------------- + TOTAL 3.54138686 570.38671268 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6598432 +BPFP 0.5235 bits/point +EBPFP 0.5235 equivalent bits/point +MSE 570.386713 +---------------------- ---------------------------------------------------------- +Time: 66.329s Load: 1.223s, Pack+Encode: 33.356s, Decode+Unpack: 31.750s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 570.3867 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.232s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 415,156B, BPFP=0.2635 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 422,444B, BPFP=0.2681 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 893,036B, BPFP=0.5669 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 880,368B, BPFP=0.5588 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,130,096B, BPFP=0.7173 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,123,028B, BPFP=0.7128 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 747,044B, BPFP=0.4742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 770,976B, BPFP=0.4894 +⌛️ [2/4] FRONTEND: Frontend time: 33.458s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.749s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561636 3.04313537 + layer.9.1 0.14552785 4.41192022 + layer.19.0 0.04069186 9.37356356 + layer.19.1 0.03840616 19.15095111 + layer.29.0 0.11346353 36.41658119 + layer.29.1 0.11182956 42.73998619 + layer.39.0 10.19697364 2271.01998700 + layer.39.1 10.11578978 1698.23350666 + ------------------------------------------------------------------------------------- + TOTAL 2.61353734 510.54870391 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6382148 +BPFP 0.5064 bits/point +EBPFP 0.5064 equivalent bits/point +MSE 510.548704 +---------------------- ---------------------------------------------------------- +Time: 66.438s Load: 1.232s, Pack+Encode: 33.458s, Decode+Unpack: 31.749s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 510.5487 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.226s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 416,880B, BPFP=0.2646 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 403,552B, BPFP=0.2562 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 950,376B, BPFP=0.6033 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 945,996B, BPFP=0.6005 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,183,508B, BPFP=0.7512 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,185,184B, BPFP=0.7523 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 780,148B, BPFP=0.4952 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 773,456B, BPFP=0.4910 +⌛️ [2/4] FRONTEND: Frontend time: 33.455s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.851s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14399445 0.40335308 + layer.9.1 0.14558028 3.04264979 + layer.19.0 0.03837104 9.57081205 + layer.19.1 0.04376782 13.71546961 + layer.29.0 0.11695251 41.92611005 + layer.29.1 0.13128335 41.05695483 + layer.39.0 11.28613757 1669.55476113 + layer.39.1 11.84408769 1674.06288593 + ------------------------------------------------------------------------------------- + TOTAL 2.96877184 431.66662456 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6639100 +BPFP 0.5268 bits/point +EBPFP 0.5268 equivalent bits/point +MSE 431.666625 +---------------------- ---------------------------------------------------------- +Time: 66.532s Load: 1.226s, Pack+Encode: 33.455s, Decode+Unpack: 31.851s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 431.6666 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 416,700B, BPFP=0.2645 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 408,776B, BPFP=0.2595 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 995,664B, BPFP=0.6320 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 994,088B, BPFP=0.6310 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,313,280B, BPFP=0.8336 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,304,160B, BPFP=0.8278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 870,620B, BPFP=0.5526 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 888,160B, BPFP=0.5638 +⌛️ [2/4] FRONTEND: Frontend time: 33.393s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.798s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14502686 4.30943448 + layer.9.1 0.03259508 4.24384229 + layer.19.0 0.11326540 11.70087291 + layer.19.1 0.11324834 11.35780463 + layer.29.0 0.12250664 114.42605419 + layer.29.1 0.12058897 110.59826942 + layer.39.0 16.17915050 2721.11748456 + layer.39.1 21.66230805 3163.08027299 + ------------------------------------------------------------------------------------- + TOTAL 4.81108623 767.60425443 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7191448 +BPFP 0.5706 bits/point +EBPFP 0.5706 equivalent bits/point +MSE 767.604254 +---------------------- ---------------------------------------------------------- +Time: 66.417s Load: 1.225s, Pack+Encode: 33.393s, Decode+Unpack: 31.798s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 767.6043 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,276B, BPFP=0.2490 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 394,560B, BPFP=0.2504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 891,324B, BPFP=0.5658 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 889,000B, BPFP=0.5643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,103,580B, BPFP=0.7005 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,105,300B, BPFP=0.7016 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 746,332B, BPFP=0.4737 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 717,120B, BPFP=0.4552 +⌛️ [2/4] FRONTEND: Frontend time: 33.731s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.749s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66733019 4.37100235 + layer.9.1 2.66763138 4.50633639 + layer.19.0 3.22293078 8.75075599 + layer.19.1 3.22376992 16.89424307 + layer.29.0 4.27658332 69.39737874 + layer.29.1 4.27160529 45.29243683 + layer.39.0 7.81683598 2246.52421189 + layer.39.1 9.86231960 1380.51446214 + ------------------------------------------------------------------------------------- + TOTAL 4.75112581 472.03135342 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6239492 +BPFP 0.4951 bits/point +EBPFP 0.4951 equivalent bits/point +MSE 472.031353 +---------------------- ---------------------------------------------------------- +Time: 66.707s Load: 1.227s, Pack+Encode: 33.731s, Decode+Unpack: 31.749s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 472.0314 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.219s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 385,532B, BPFP=0.2447 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 389,244B, BPFP=0.2471 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 844,892B, BPFP=0.5363 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 836,724B, BPFP=0.5311 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,038,648B, BPFP=0.6593 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,025,872B, BPFP=0.6512 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 651,692B, BPFP=0.4137 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 686,440B, BPFP=0.4357 +⌛️ [2/4] FRONTEND: Frontend time: 33.446s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.773s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549763 0.34661367 + layer.9.1 0.14520254 3.06220198 + layer.19.0 0.04746155 25.73172936 + layer.19.1 0.04383140 28.84813485 + layer.29.0 4.26247378 76.08347213 + layer.29.1 4.25497898 86.86909937 + layer.39.0 7.94138086 1200.17744556 + layer.39.1 7.86439079 1514.43873903 + ------------------------------------------------------------------------------------- + TOTAL 3.08815219 366.94467950 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5859044 +BPFP 0.4649 bits/point +EBPFP 0.4649 equivalent bits/point +MSE 366.944679 +---------------------- ---------------------------------------------------------- +Time: 66.437s Load: 1.219s, Pack+Encode: 33.446s, Decode+Unpack: 31.773s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 366.9447 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 376,132B, BPFP=0.2387 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 383,644B, BPFP=0.2435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 812,708B, BPFP=0.5159 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 868,364B, BPFP=0.5512 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,009,060B, BPFP=0.6405 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,033,760B, BPFP=0.6562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 711,328B, BPFP=0.4515 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 707,192B, BPFP=0.4489 +⌛️ [2/4] FRONTEND: Frontend time: 33.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.781s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11283708 0.35739101 + layer.9.1 0.11300174 4.31616758 + layer.19.0 3.22718329 25.81741550 + layer.19.1 3.22892155 16.16861746 + layer.29.0 4.26448309 38.56378727 + layer.29.1 4.25758082 94.76485822 + layer.39.0 9.82393946 2410.14803380 + layer.39.1 9.78394007 1930.55866103 + ------------------------------------------------------------------------------------- + TOTAL 4.35148589 565.08686649 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 5902188 +BPFP 0.4683 bits/point +EBPFP 0.4683 equivalent bits/point +MSE 565.086866 +---------------------- ---------------------------------------------------------- +Time: 66.513s Load: 1.225s, Pack+Encode: 33.506s, Decode+Unpack: 31.781s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.0869 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.247s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 484,844B, BPFP=0.3078 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 477,956B, BPFP=0.3034 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 965,512B, BPFP=0.6129 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 959,524B, BPFP=0.6091 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,183,276B, BPFP=0.7511 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,185,772B, BPFP=0.7527 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 811,448B, BPFP=0.5151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 831,332B, BPFP=0.5277 +⌛️ [2/4] FRONTEND: Frontend time: 33.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.861s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14503861 4.39216839 + layer.9.1 0.14483112 4.30506867 + layer.19.0 0.11529889 14.42420453 + layer.19.1 0.11517203 28.23201881 + layer.29.0 0.11961639 110.78316948 + layer.29.1 0.11795276 127.21232125 + layer.39.0 83.84633978 2823.63698408 + layer.39.1 174.87768118 3319.18492038 + ------------------------------------------------------------------------------------- + TOTAL 32.43524135 804.02135695 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6899664 +BPFP 0.5474 bits/point +EBPFP 0.5474 equivalent bits/point +MSE 804.021357 +---------------------- ---------------------------------------------------------- +Time: 66.720s Load: 1.247s, Pack+Encode: 33.612s, Decode+Unpack: 31.861s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 804.0214 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 408,372B, BPFP=0.2592 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 406,732B, BPFP=0.2582 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 881,392B, BPFP=0.5595 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 888,296B, BPFP=0.5638 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,120,404B, BPFP=0.7112 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,125,900B, BPFP=0.7147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 749,776B, BPFP=0.4759 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 760,956B, BPFP=0.4830 +⌛️ [2/4] FRONTEND: Frontend time: 33.332s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.765s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.81453913 12.48610533 + layer.9.1 0.14528001 4.35302147 + layer.19.0 3.26598681 25.47792848 + layer.19.1 0.04116655 17.52254123 + layer.29.0 4.28557138 44.13305370 + layer.29.1 4.28198282 47.14276284 + layer.39.0 74.89367180 1611.67533312 + layer.39.1 42.04871577 2531.96132597 + ------------------------------------------------------------------------------------- + TOTAL 16.47211428 536.84400902 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6341828 +BPFP 0.5032 bits/point +EBPFP 0.5032 equivalent bits/point +MSE 536.844009 +---------------------- ---------------------------------------------------------- +Time: 66.312s Load: 1.215s, Pack+Encode: 33.332s, Decode+Unpack: 31.765s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 536.8440 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.222s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 396,256B, BPFP=0.2515 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 404,644B, BPFP=0.2568 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 933,500B, BPFP=0.5925 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 924,640B, BPFP=0.5869 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,136,388B, BPFP=0.7213 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,123,240B, BPFP=0.7130 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 716,124B, BPFP=0.4546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 703,776B, BPFP=0.4467 +⌛️ [2/4] FRONTEND: Frontend time: 33.351s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.742s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66710422 3.04932577 + layer.9.1 2.66812426 0.35490934 + layer.19.0 3.22059776 20.11899019 + layer.19.1 3.22546153 21.59366875 + layer.29.0 0.11226317 35.67419565 + layer.29.1 0.11257672 35.00798007 + layer.39.0 59.39237691 2028.56613585 + layer.39.1 37.52358222 1372.23139422 + ------------------------------------------------------------------------------------- + TOTAL 13.61526085 439.57457498 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6338568 +BPFP 0.5029 bits/point +EBPFP 0.5029 equivalent bits/point +MSE 439.574575 +---------------------- ---------------------------------------------------------- +Time: 66.316s Load: 1.222s, Pack+Encode: 33.351s, Decode+Unpack: 31.742s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 439.5746 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,104B, BPFP=0.2489 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 389,044B, BPFP=0.2469 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 932,476B, BPFP=0.5919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 929,636B, BPFP=0.5901 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,142,028B, BPFP=0.7249 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,149,360B, BPFP=0.7296 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 699,024B, BPFP=0.4437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 693,656B, BPFP=0.4403 +⌛️ [2/4] FRONTEND: Frontend time: 33.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.709s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519123 4.34458946 + layer.9.1 0.14511500 0.38548424 + layer.19.0 0.03974548 13.93008485 + layer.19.1 0.03981401 9.75287034 + layer.29.0 4.26343511 37.94587362 + layer.29.1 4.25610090 36.11893179 + layer.39.0 7.90972018 1243.78997400 + layer.39.1 8.05601540 1389.83084173 + ------------------------------------------------------------------------------------- + TOTAL 3.10689216 342.01233125 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6327328 +BPFP 0.5020 bits/point +EBPFP 0.5020 equivalent bits/point +MSE 342.012331 +---------------------- ---------------------------------------------------------- +Time: 66.540s Load: 1.227s, Pack+Encode: 33.605s, Decode+Unpack: 31.709s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 342.0123 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,876B, BPFP=0.2494 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 391,984B, BPFP=0.2488 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 901,668B, BPFP=0.5723 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 911,964B, BPFP=0.5789 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,180,112B, BPFP=0.7491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,184,720B, BPFP=0.7520 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 729,996B, BPFP=0.4634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 716,152B, BPFP=0.4546 +⌛️ [2/4] FRONTEND: Frontend time: 33.432s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.786s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14586543 4.39691283 + layer.9.1 0.14572574 0.36094835 + layer.19.0 0.03953905 13.09995024 + layer.19.1 0.03760033 15.45664913 + layer.29.0 0.10448607 61.75502214 + layer.29.1 0.10697372 60.34315587 + layer.39.0 14.19073468 2377.26649334 + layer.39.1 8.92149669 1955.12690933 + ------------------------------------------------------------------------------------- + TOTAL 2.96155271 560.97575515 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6409472 +BPFP 0.5086 bits/point +EBPFP 0.5086 equivalent bits/point +MSE 560.975755 +---------------------- ---------------------------------------------------------- +Time: 66.446s Load: 1.228s, Pack+Encode: 33.432s, Decode+Unpack: 31.786s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 560.9758 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 420,452B, BPFP=0.2669 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 413,792B, BPFP=0.2627 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 972,552B, BPFP=0.6173 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 967,188B, BPFP=0.6139 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,189,740B, BPFP=0.7552 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,186,544B, BPFP=0.7532 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 763,884B, BPFP=0.4849 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 761,568B, BPFP=0.4834 +⌛️ [2/4] FRONTEND: Frontend time: 33.332s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.767s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00251883 4.28450151 + layer.9.1 0.14409062 4.40772103 + layer.19.0 0.12740102 12.89521551 + layer.19.1 0.12254588 15.66846766 + layer.29.0 4.25147928 37.99059555 + layer.29.1 4.25065697 40.70648663 + layer.39.0 9.21805114 2295.18865778 + layer.39.1 9.03214690 1513.05541111 + ------------------------------------------------------------------------------------- + TOTAL 3.39361133 490.52463210 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6675720 +BPFP 0.5297 bits/point +EBPFP 0.5297 equivalent bits/point +MSE 490.524632 +---------------------- ---------------------------------------------------------- +Time: 66.314s Load: 1.215s, Pack+Encode: 33.332s, Decode+Unpack: 31.767s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 490.5246 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.229s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 478,672B, BPFP=0.3038 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 471,216B, BPFP=0.2991 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,006,332B, BPFP=0.6388 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,000,564B, BPFP=0.6351 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,245,736B, BPFP=0.7907 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,237,332B, BPFP=0.7854 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 847,104B, BPFP=0.5377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 836,668B, BPFP=0.5311 +⌛️ [2/4] FRONTEND: Frontend time: 33.764s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.794s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550905 3.01971438 + layer.9.1 0.14590163 4.47762317 + layer.19.0 0.12839093 18.50332990 + layer.19.1 0.12422524 27.66890945 + layer.29.0 0.11695262 140.86153315 + layer.29.1 0.11389293 107.73903152 + layer.39.0 10.18180439 2876.44653884 + layer.39.1 10.42432323 3709.09034774 + ------------------------------------------------------------------------------------- + TOTAL 2.67262500 860.97587852 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7123624 +BPFP 0.5652 bits/point +EBPFP 0.5652 equivalent bits/point +MSE 860.975879 +---------------------- ---------------------------------------------------------- +Time: 66.787s Load: 1.229s, Pack+Encode: 33.764s, Decode+Unpack: 31.794s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 860.9759 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.217s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 434,344B, BPFP=0.2757 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 433,076B, BPFP=0.2749 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 973,108B, BPFP=0.6177 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 978,212B, BPFP=0.6209 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,235,576B, BPFP=0.7843 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,238,260B, BPFP=0.7860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 800,152B, BPFP=0.5079 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 798,892B, BPFP=0.5071 +⌛️ [2/4] FRONTEND: Frontend time: 33.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.763s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509775 0.42172849 + layer.9.1 0.14508723 0.36570203 + layer.19.0 0.11633494 9.23981354 + layer.19.1 0.11804005 19.48127488 + layer.29.0 0.15409572 47.25374248 + layer.29.1 0.14997486 38.41754093 + layer.39.0 9.23291952 2562.17078323 + layer.39.1 9.22304726 2614.63064673 + ------------------------------------------------------------------------------------- + TOTAL 2.41057467 661.49765404 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6891620 +BPFP 0.5468 bits/point +EBPFP 0.5468 equivalent bits/point +MSE 661.497654 +---------------------- ---------------------------------------------------------- +Time: 66.529s Load: 1.217s, Pack+Encode: 33.550s, Decode+Unpack: 31.763s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 661.4977 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.246s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 448,840B, BPFP=0.2849 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 452,680B, BPFP=0.2873 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 985,540B, BPFP=0.6256 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 991,928B, BPFP=0.6296 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,237,952B, BPFP=0.7858 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,245,280B, BPFP=0.7904 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 808,824B, BPFP=0.5134 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 837,032B, BPFP=0.5313 +⌛️ [2/4] FRONTEND: Frontend time: 33.441s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.744s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497060 8.54986150 + layer.9.1 0.14492971 8.42175313 + layer.19.0 0.11929473 28.04202043 + layer.19.1 0.11869117 7.71150116 + layer.29.0 0.13715227 57.32973473 + layer.29.1 0.14278979 53.91859969 + layer.39.0 9.99110525 2432.22001950 + layer.39.1 10.01170034 3164.22326942 + ------------------------------------------------------------------------------------- + TOTAL 2.60132923 720.05209494 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7008076 +BPFP 0.5560 bits/point +EBPFP 0.5560 equivalent bits/point +MSE 720.052095 +---------------------- ---------------------------------------------------------- +Time: 66.431s Load: 1.246s, Pack+Encode: 33.441s, Decode+Unpack: 31.744s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 720.0521 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,716B, BPFP=0.2734 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 422,536B, BPFP=0.2682 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 955,104B, BPFP=0.6063 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 969,404B, BPFP=0.6153 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,223,840B, BPFP=0.7768 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,240,220B, BPFP=0.7872 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 849,716B, BPFP=0.5394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 860,760B, BPFP=0.5464 +⌛️ [2/4] FRONTEND: Frontend time: 33.815s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.780s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14589856 4.39390602 + layer.9.1 0.03321603 0.35081397 + layer.19.0 0.11866178 8.91147399 + layer.19.1 0.11267978 16.82342150 + layer.29.0 0.10803594 61.22751462 + layer.29.1 0.10714094 129.17222538 + layer.39.0 11.58943751 2832.89762756 + layer.39.1 9.70079103 3488.03639909 + ------------------------------------------------------------------------------------- + TOTAL 2.73948269 817.72667277 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6952296 +BPFP 0.5516 bits/point +EBPFP 0.5516 equivalent bits/point +MSE 817.726673 +---------------------- ---------------------------------------------------------- +Time: 66.817s Load: 1.221s, Pack+Encode: 33.815s, Decode+Unpack: 31.780s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 817.7267 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,292B, BPFP=0.2522 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 403,936B, BPFP=0.2564 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 938,704B, BPFP=0.5958 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 940,128B, BPFP=0.5967 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,197,764B, BPFP=0.7603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,197,752B, BPFP=0.7603 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 789,396B, BPFP=0.5011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 776,808B, BPFP=0.4931 +⌛️ [2/4] FRONTEND: Frontend time: 33.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.820s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03308301 0.39702598 + layer.9.1 0.14566304 4.40123738 + layer.19.0 0.03810260 13.21199499 + layer.19.1 0.03780774 10.51957187 + layer.29.0 0.11592613 36.53863849 + layer.29.1 0.11717217 49.35783637 + layer.39.0 9.98032847 2984.43223919 + layer.39.1 9.70849498 2055.61504712 + ------------------------------------------------------------------------------------- + TOTAL 2.52207227 644.30919892 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6641780 +BPFP 0.5270 bits/point +EBPFP 0.5270 equivalent bits/point +MSE 644.309199 +---------------------- ---------------------------------------------------------- +Time: 66.595s Load: 1.221s, Pack+Encode: 33.554s, Decode+Unpack: 31.820s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 644.3092 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.231s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 399,696B, BPFP=0.2537 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 392,252B, BPFP=0.2490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 916,368B, BPFP=0.5817 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 912,228B, BPFP=0.5790 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,118,808B, BPFP=0.7102 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,107,396B, BPFP=0.7029 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 725,308B, BPFP=0.4604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 727,872B, BPFP=0.4620 +⌛️ [2/4] FRONTEND: Frontend time: 33.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.735s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14555504 0.40618549 + layer.9.1 0.14557384 0.35468737 + layer.19.0 0.03995539 14.58714099 + layer.19.1 0.04542811 13.59194985 + layer.29.0 0.12033866 45.57058417 + layer.29.1 0.13252172 37.51274324 + layer.39.0 10.37566776 1940.89811505 + layer.39.1 9.84188447 1920.11244719 + ------------------------------------------------------------------------------------- + TOTAL 2.60586562 496.62923167 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6299928 +BPFP 0.4999 bits/point +EBPFP 0.4999 equivalent bits/point +MSE 496.629232 +---------------------- ---------------------------------------------------------- +Time: 66.264s Load: 1.231s, Pack+Encode: 33.297s, Decode+Unpack: 31.735s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 496.6292 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 458,480B, BPFP=0.2910 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 446,992B, BPFP=0.2837 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 995,396B, BPFP=0.6318 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 987,116B, BPFP=0.6266 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,245,416B, BPFP=0.7905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,244,072B, BPFP=0.7897 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 939,500B, BPFP=0.5963 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 895,936B, BPFP=0.5687 +⌛️ [2/4] FRONTEND: Frontend time: 33.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.740s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14470203 3.00045797 + layer.9.1 0.14481130 0.36005815 + layer.19.0 0.11257574 14.28400101 + layer.19.1 0.11422884 17.56287323 + layer.29.0 0.10456927 129.85725747 + layer.29.1 0.10551051 69.27935591 + layer.39.0 10.36536069 3583.37244069 + layer.39.1 11.81531702 3543.90185245 + ------------------------------------------------------------------------------------- + TOTAL 2.86338443 920.20228711 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7212908 +BPFP 0.5723 bits/point +EBPFP 0.5723 equivalent bits/point +MSE 920.202287 +---------------------- ---------------------------------------------------------- +Time: 66.607s Load: 1.223s, Pack+Encode: 33.644s, Decode+Unpack: 31.740s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 920.2023 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.220s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 446,120B, BPFP=0.2832 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 445,984B, BPFP=0.2831 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,013,756B, BPFP=0.6435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 985,280B, BPFP=0.6254 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,176,096B, BPFP=0.7465 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,156,980B, BPFP=0.7344 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 783,388B, BPFP=0.4973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 783,088B, BPFP=0.4971 +⌛️ [2/4] FRONTEND: Frontend time: 33.376s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.798s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14530003 3.31959365 + layer.9.1 0.14546206 3.04171004 + layer.19.0 0.11891763 9.71685083 + layer.19.1 0.11677460 20.80082822 + layer.29.0 4.29725807 42.69181122 + layer.29.1 4.29692800 77.40361960 + layer.39.0 11.61914761 2795.27136822 + layer.39.1 11.22064282 2728.39876503 + ------------------------------------------------------------------------------------- + TOTAL 3.99505385 710.08056835 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6790692 +BPFP 0.5388 bits/point +EBPFP 0.5388 equivalent bits/point +MSE 710.080568 +---------------------- ---------------------------------------------------------- +Time: 66.394s Load: 1.220s, Pack+Encode: 33.376s, Decode+Unpack: 31.798s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 710.0806 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.233s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 395,088B, BPFP=0.2508 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 385,104B, BPFP=0.2444 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 870,972B, BPFP=0.5528 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 880,696B, BPFP=0.5590 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,078,136B, BPFP=0.6843 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,079,136B, BPFP=0.6850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 760,120B, BPFP=0.4825 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 767,360B, BPFP=0.4871 +⌛️ [2/4] FRONTEND: Frontend time: 33.442s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.751s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67106792 4.38338727 + layer.9.1 2.67195307 4.38172993 + layer.19.0 0.08237472 22.23046494 + layer.19.1 0.08192194 15.80578943 + layer.29.0 0.11152953 83.18154859 + layer.29.1 0.11703055 155.40152746 + layer.39.0 163.01811830 1674.31654209 + layer.39.1 58.15221299 1874.17565811 + ------------------------------------------------------------------------------------- + TOTAL 28.36327613 479.23458098 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6216612 +BPFP 0.4932 bits/point +EBPFP 0.4932 equivalent bits/point +MSE 479.234581 +---------------------- ---------------------------------------------------------- +Time: 66.426s Load: 1.233s, Pack+Encode: 33.442s, Decode+Unpack: 31.751s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 479.2346 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 429,592B, BPFP=0.2727 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 437,148B, BPFP=0.2775 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 942,448B, BPFP=0.5982 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 921,484B, BPFP=0.5849 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,146,236B, BPFP=0.7276 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,138,980B, BPFP=0.7230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 755,048B, BPFP=0.4793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 765,380B, BPFP=0.4858 +⌛️ [2/4] FRONTEND: Frontend time: 33.332s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.750s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14620710 4.39874535 + layer.9.1 0.14642976 0.39343563 + layer.19.0 0.11726453 20.33692771 + layer.19.1 0.11958517 15.41221157 + layer.29.0 0.10693079 47.08157296 + layer.29.1 0.10826971 77.22578811 + layer.39.0 43.01306569 1948.31215470 + layer.39.1 17.12450997 1843.95271368 + ------------------------------------------------------------------------------------- + TOTAL 7.61028284 494.63919371 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6536316 +BPFP 0.5186 bits/point +EBPFP 0.5186 equivalent bits/point +MSE 494.639194 +---------------------- ---------------------------------------------------------- +Time: 66.310s Load: 1.228s, Pack+Encode: 33.332s, Decode+Unpack: 31.750s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 494.6392 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.243s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 393,300B, BPFP=0.2496 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 385,808B, BPFP=0.2449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 890,276B, BPFP=0.5651 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 882,420B, BPFP=0.5601 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,094,772B, BPFP=0.6949 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,081,884B, BPFP=0.6867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 709,752B, BPFP=0.4505 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 735,480B, BPFP=0.4668 +⌛️ [2/4] FRONTEND: Frontend time: 33.347s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.741s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03173930 0.33935892 + layer.9.1 0.03345565 4.38031984 + layer.19.0 3.26068347 15.01036419 + layer.19.1 3.26087326 19.09541812 + layer.29.0 4.24610771 31.03339799 + layer.29.1 4.24089229 35.84801044 + layer.39.0 8.81319124 1664.00146246 + layer.39.1 8.71779153 2074.72521937 + ------------------------------------------------------------------------------------- + TOTAL 4.07559181 480.55419392 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6173692 +BPFP 0.4898 bits/point +EBPFP 0.4898 equivalent bits/point +MSE 480.554194 +---------------------- ---------------------------------------------------------- +Time: 66.331s Load: 1.243s, Pack+Encode: 33.347s, Decode+Unpack: 31.741s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 480.5542 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.224s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,608B, BPFP=0.2524 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 390,176B, BPFP=0.2477 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 933,760B, BPFP=0.5927 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 933,604B, BPFP=0.5926 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,191,376B, BPFP=0.7562 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,197,580B, BPFP=0.7602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 778,660B, BPFP=0.4943 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 755,012B, BPFP=0.4792 +⌛️ [2/4] FRONTEND: Frontend time: 33.332s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.756s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078599 4.34189685 + layer.9.1 0.00079117 4.40594405 + layer.19.0 0.00795310 17.00215815 + layer.19.1 0.00811505 10.26423363 + layer.29.0 4.25797468 37.77303126 + layer.29.1 4.25504309 43.17831390 + layer.39.0 81.06806549 1842.50698733 + layer.39.1 44.82015254 1540.80597985 + ------------------------------------------------------------------------------------- + TOTAL 16.80236014 437.53481813 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6577776 +BPFP 0.5219 bits/point +EBPFP 0.5219 equivalent bits/point +MSE 437.534818 +---------------------- ---------------------------------------------------------- +Time: 66.312s Load: 1.224s, Pack+Encode: 33.332s, Decode+Unpack: 31.756s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 437.5348 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 415,644B, BPFP=0.2638 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 406,724B, BPFP=0.2582 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 978,208B, BPFP=0.6209 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 982,212B, BPFP=0.6235 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,236,428B, BPFP=0.7848 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,240,628B, BPFP=0.7875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 788,232B, BPFP=0.5003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 788,176B, BPFP=0.5003 +⌛️ [2/4] FRONTEND: Frontend time: 33.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.782s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00082351 4.42637982 + layer.9.1 0.02968625 3.04017839 + layer.19.0 0.00841222 18.24634892 + layer.19.1 0.03743129 8.30791584 + layer.29.0 4.28408194 36.60958828 + layer.29.1 4.28564945 32.60978124 + layer.39.0 8.35370986 1446.33766656 + layer.39.1 8.52557915 1443.05378616 + ------------------------------------------------------------------------------------- + TOTAL 3.19067171 374.07895565 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6836252 +BPFP 0.5424 bits/point +EBPFP 0.5424 equivalent bits/point +MSE 374.078956 +---------------------- ---------------------------------------------------------- +Time: 66.600s Load: 1.227s, Pack+Encode: 33.590s, Decode+Unpack: 31.782s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 374.0790 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 421,616B, BPFP=0.2676 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 424,836B, BPFP=0.2697 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 945,388B, BPFP=0.6001 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 956,456B, BPFP=0.6071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,189,864B, BPFP=0.7553 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,195,492B, BPFP=0.7588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 782,296B, BPFP=0.4966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 801,416B, BPFP=0.5087 +⌛️ [2/4] FRONTEND: Frontend time: 33.341s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.782s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14529990 4.28635942 + layer.9.1 0.14524076 0.34963254 + layer.19.0 0.03780325 21.32884861 + layer.19.1 0.03783790 7.53467384 + layer.29.0 4.32098184 46.47271084 + layer.29.1 4.32100596 44.36184799 + layer.39.0 9.32673680 3081.86155346 + layer.39.1 9.31823369 3198.90802730 + ------------------------------------------------------------------------------------- + TOTAL 3.45664251 800.63795675 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6717364 +BPFP 0.5330 bits/point +EBPFP 0.5330 equivalent bits/point +MSE 800.637957 +---------------------- ---------------------------------------------------------- +Time: 66.348s Load: 1.225s, Pack+Encode: 33.341s, Decode+Unpack: 31.782s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 800.6380 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,948B, BPFP=0.2735 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 435,916B, BPFP=0.2767 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 954,300B, BPFP=0.6057 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 948,368B, BPFP=0.6020 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,220,256B, BPFP=0.7746 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,205,788B, BPFP=0.7654 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 788,396B, BPFP=0.5004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 802,500B, BPFP=0.5094 +⌛️ [2/4] FRONTEND: Frontend time: 33.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.769s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497286 0.35763388 + layer.9.1 0.14497296 3.01752385 + layer.19.0 0.03962668 25.25890427 + layer.19.1 0.11751332 11.38303339 + layer.29.0 0.14529291 54.49780123 + layer.29.1 0.16241527 54.67094573 + layer.39.0 11.40179406 2471.32287943 + layer.39.1 13.03458244 2183.69207020 + ------------------------------------------------------------------------------------- + TOTAL 3.14889631 600.52509900 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6786472 +BPFP 0.5385 bits/point +EBPFP 0.5385 equivalent bits/point +MSE 600.525099 +---------------------- ---------------------------------------------------------- +Time: 66.505s Load: 1.221s, Pack+Encode: 33.515s, Decode+Unpack: 31.769s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 600.5251 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 446,196B, BPFP=0.2832 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 448,120B, BPFP=0.2844 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,010,092B, BPFP=0.6412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,007,716B, BPFP=0.6396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,258,100B, BPFP=0.7986 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,269,768B, BPFP=0.8060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 828,740B, BPFP=0.5260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 839,084B, BPFP=0.5326 +⌛️ [2/4] FRONTEND: Frontend time: 33.368s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.778s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14513344 0.35689618 + layer.9.1 0.03283094 4.23085595 + layer.19.0 0.11544709 18.74141311 + layer.19.1 0.11326018 27.75128473 + layer.29.0 0.14483232 48.24143342 + layer.29.1 0.14672551 40.72389401 + layer.39.0 10.02784076 2664.28891778 + layer.39.1 15.62606130 3153.57621059 + ------------------------------------------------------------------------------------- + TOTAL 3.29401644 744.73886322 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7107816 +BPFP 0.5640 bits/point +EBPFP 0.5640 equivalent bits/point +MSE 744.738863 +---------------------- ---------------------------------------------------------- +Time: 66.374s Load: 1.228s, Pack+Encode: 33.368s, Decode+Unpack: 31.778s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 744.7389 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 454,308B, BPFP=0.2884 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 459,904B, BPFP=0.2919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,020,288B, BPFP=0.6476 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,026,888B, BPFP=0.6518 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,259,028B, BPFP=0.7992 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,265,336B, BPFP=0.8032 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 837,032B, BPFP=0.5313 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 851,116B, BPFP=0.5402 +⌛️ [2/4] FRONTEND: Frontend time: 33.471s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.721s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492303 12.48239585 + layer.9.1 0.14484742 4.31589623 + layer.19.0 0.11740684 17.49629560 + layer.19.1 0.11489933 18.08202871 + layer.29.0 0.12072669 81.11298546 + layer.29.1 0.12118037 60.40641250 + layer.39.0 10.74778980 2060.04403640 + layer.39.1 11.83662176 2972.46506337 + ------------------------------------------------------------------------------------- + TOTAL 2.91854940 653.30063926 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7173900 +BPFP 0.5692 bits/point +EBPFP 0.5692 equivalent bits/point +MSE 653.300639 +---------------------- ---------------------------------------------------------- +Time: 66.420s Load: 1.228s, Pack+Encode: 33.471s, Decode+Unpack: 31.721s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 653.3006 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.235s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 483,004B, BPFP=0.3066 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 479,844B, BPFP=0.3046 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,056,192B, BPFP=0.6704 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,051,176B, BPFP=0.6672 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,294,896B, BPFP=0.8219 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,296,904B, BPFP=0.8232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 867,780B, BPFP=0.5508 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 882,072B, BPFP=0.5599 +⌛️ [2/4] FRONTEND: Frontend time: 33.433s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.838s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14465000 4.20489659 + layer.9.1 0.14489275 0.37492950 + layer.19.0 0.11978787 17.77792899 + layer.19.1 0.12819003 10.41277586 + layer.29.0 0.12519148 58.38286074 + layer.29.1 0.13018718 59.58397993 + layer.39.0 10.77894586 3147.63080923 + layer.39.1 10.25834823 3182.18979526 + ------------------------------------------------------------------------------------- + TOTAL 2.72877418 810.06974701 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7411868 +BPFP 0.5881 bits/point +EBPFP 0.5881 equivalent bits/point +MSE 810.069747 +---------------------- ---------------------------------------------------------- +Time: 66.506s Load: 1.235s, Pack+Encode: 33.433s, Decode+Unpack: 31.838s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 810.0697 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.260s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 403,796B, BPFP=0.2563 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 400,776B, BPFP=0.2544 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 934,128B, BPFP=0.5929 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 911,592B, BPFP=0.5786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,133,032B, BPFP=0.7192 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,117,404B, BPFP=0.7093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 761,472B, BPFP=0.4833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 741,144B, BPFP=0.4704 +⌛️ [2/4] FRONTEND: Frontend time: 33.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.761s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14553812 4.36584532 + layer.9.1 0.14559401 0.35872041 + layer.19.0 0.04492324 14.53991053 + layer.19.1 0.04213941 19.49935509 + layer.29.0 4.25320263 40.79756815 + layer.29.1 4.25391672 37.93940679 + layer.39.0 8.72311137 1498.60708482 + layer.39.1 8.87262096 1521.01446214 + ------------------------------------------------------------------------------------- + TOTAL 3.31013081 392.14029416 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6403344 +BPFP 0.5081 bits/point +EBPFP 0.5081 equivalent bits/point +MSE 392.140294 +---------------------- ---------------------------------------------------------- +Time: 66.669s Load: 1.260s, Pack+Encode: 33.648s, Decode+Unpack: 31.761s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 392.1403 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 439,704B, BPFP=0.2791 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 435,020B, BPFP=0.2761 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 963,468B, BPFP=0.6116 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 972,816B, BPFP=0.6175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,217,776B, BPFP=0.7730 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,218,420B, BPFP=0.7734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 787,532B, BPFP=0.4999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 789,868B, BPFP=0.5014 +⌛️ [2/4] FRONTEND: Frontend time: 33.372s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.823s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14518122 4.33174720 + layer.9.1 0.14529820 3.02537285 + layer.19.0 0.11833418 10.25747291 + layer.19.1 0.12038008 10.09963286 + layer.29.0 4.31360161 47.95341445 + layer.29.1 4.31792870 51.25232572 + layer.39.0 9.40764201 1824.90331492 + layer.39.1 11.30764416 2624.29249269 + ------------------------------------------------------------------------------------- + TOTAL 3.73450127 572.01447170 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6824604 +BPFP 0.5415 bits/point +EBPFP 0.5415 equivalent bits/point +MSE 572.014472 +---------------------- ---------------------------------------------------------- +Time: 66.410s Load: 1.215s, Pack+Encode: 33.372s, Decode+Unpack: 31.823s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.0145 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.232s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 478,248B, BPFP=0.3036 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 476,736B, BPFP=0.3026 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,023,208B, BPFP=0.6495 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,022,660B, BPFP=0.6491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,256,136B, BPFP=0.7973 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,266,128B, BPFP=0.8037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 829,460B, BPFP=0.5265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 787,776B, BPFP=0.5000 +⌛️ [2/4] FRONTEND: Frontend time: 33.372s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.794s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00315138 4.53137473 + layer.9.1 0.00505826 3.05856265 + layer.19.0 0.09147678 26.20769266 + layer.19.1 0.09143778 12.73950251 + layer.29.0 0.11015094 227.22582873 + layer.29.1 0.11338039 139.56377966 + layer.39.0 9.14784464 2239.29639259 + layer.39.1 8.98944348 1743.12138447 + ------------------------------------------------------------------------------------- + TOTAL 2.31899296 549.46806475 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7140352 +BPFP 0.5665 bits/point +EBPFP 0.5665 equivalent bits/point +MSE 549.468065 +---------------------- ---------------------------------------------------------- +Time: 66.398s Load: 1.232s, Pack+Encode: 33.372s, Decode+Unpack: 31.794s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 549.4681 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 505,068B, BPFP=0.3206 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 513,260B, BPFP=0.3258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,076,032B, BPFP=0.6830 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,072,264B, BPFP=0.6806 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,334,492B, BPFP=0.8471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,326,260B, BPFP=0.8418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 891,112B, BPFP=0.5656 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 895,144B, BPFP=0.5682 +⌛️ [2/4] FRONTEND: Frontend time: 33.404s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.846s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14591913 3.05802819 + layer.9.1 0.03347605 4.31995418 + layer.19.0 0.12173996 10.78183397 + layer.19.1 0.12099332 9.81552458 + layer.29.0 0.11078974 94.20368866 + layer.29.1 0.11776269 113.20868541 + layer.39.0 10.17800795 3678.86707832 + layer.39.1 9.88744998 3449.45563861 + ------------------------------------------------------------------------------------- + TOTAL 2.58951735 920.46380399 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7613632 +BPFP 0.6041 bits/point +EBPFP 0.6041 equivalent bits/point +MSE 920.463804 +---------------------- ---------------------------------------------------------- +Time: 66.478s Load: 1.228s, Pack+Encode: 33.404s, Decode+Unpack: 31.846s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 920.4638 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.226s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 405,600B, BPFP=0.2575 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 398,308B, BPFP=0.2528 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 961,800B, BPFP=0.6105 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 964,440B, BPFP=0.6122 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,249,148B, BPFP=0.7929 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,256,236B, BPFP=0.7974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 766,244B, BPFP=0.4864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 743,196B, BPFP=0.4717 +⌛️ [2/4] FRONTEND: Frontend time: 33.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.725s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66405266 4.50937303 + layer.9.1 2.66543197 4.44928952 + layer.19.0 3.22131407 13.56228419 + layer.19.1 3.22426883 8.91607529 + layer.29.0 4.27224607 39.96483486 + layer.29.1 4.27784520 40.02126412 + layer.39.0 8.94937744 2879.50601235 + layer.39.1 8.82170070 1916.73903152 + ------------------------------------------------------------------------------------- + TOTAL 4.76202962 613.45852061 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6744972 +BPFP 0.5352 bits/point +EBPFP 0.5352 equivalent bits/point +MSE 613.458521 +---------------------- ---------------------------------------------------------- +Time: 66.605s Load: 1.226s, Pack+Encode: 33.654s, Decode+Unpack: 31.725s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 613.4585 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 432,892B, BPFP=0.2748 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 426,768B, BPFP=0.2709 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 980,784B, BPFP=0.6226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 970,436B, BPFP=0.6160 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,257,684B, BPFP=0.7983 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,239,036B, BPFP=0.7865 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 790,048B, BPFP=0.5015 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 809,320B, BPFP=0.5137 +⌛️ [2/4] FRONTEND: Frontend time: 33.464s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.785s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00104424 3.07954716 + layer.9.1 0.00091568 4.44188263 + layer.19.0 0.08171424 7.92138053 + layer.19.1 0.08373584 9.38050709 + layer.29.0 4.26071267 32.89768342 + layer.29.1 4.26438533 33.35433509 + layer.39.0 8.39843369 1570.58612285 + layer.39.1 8.51949380 2172.21156971 + ------------------------------------------------------------------------------------- + TOTAL 3.20130444 479.23412856 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6906968 +BPFP 0.5480 bits/point +EBPFP 0.5480 equivalent bits/point +MSE 479.234129 +---------------------- ---------------------------------------------------------- +Time: 66.475s Load: 1.225s, Pack+Encode: 33.464s, Decode+Unpack: 31.785s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 479.2341 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.240s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 465,448B, BPFP=0.2954 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 461,628B, BPFP=0.2930 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,018,408B, BPFP=0.6464 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,018,884B, BPFP=0.6467 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,282,096B, BPFP=0.8138 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,289,628B, BPFP=0.8186 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 811,396B, BPFP=0.5150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 854,700B, BPFP=0.5425 +⌛️ [2/4] FRONTEND: Frontend time: 33.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.846s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14622840 4.26901171 + layer.9.1 0.03344178 0.37677865 + layer.19.0 0.12675888 11.40307752 + layer.19.1 0.12382618 15.26918214 + layer.29.0 0.12223263 84.76855500 + layer.29.1 0.12797405 62.62125244 + layer.39.0 10.69978368 2156.44215145 + layer.39.1 8.63538768 3229.72213195 + ------------------------------------------------------------------------------------- + TOTAL 2.50195416 695.60901761 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7202188 +BPFP 0.5714 bits/point +EBPFP 0.5714 equivalent bits/point +MSE 695.609018 +---------------------- ---------------------------------------------------------- +Time: 66.740s Load: 1.240s, Pack+Encode: 33.654s, Decode+Unpack: 31.846s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 695.6090 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 460,480B, BPFP=0.2923 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 462,496B, BPFP=0.2936 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 977,392B, BPFP=0.6204 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 987,264B, BPFP=0.6267 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,225,016B, BPFP=0.7776 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,227,028B, BPFP=0.7789 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 794,312B, BPFP=0.5042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 784,580B, BPFP=0.4980 +⌛️ [2/4] FRONTEND: Frontend time: 33.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.782s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492994 3.15349201 + layer.9.1 0.14498602 0.36473304 + layer.19.0 0.12957112 11.60809281 + layer.19.1 0.13054295 27.68220365 + layer.29.0 0.16610158 110.81995450 + layer.29.1 0.14872770 53.98636558 + layer.39.0 16.52878844 2274.72976926 + layer.39.1 24.55764797 2762.50861228 + ------------------------------------------------------------------------------------- + TOTAL 5.24391197 655.60665289 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 6918568 +BPFP 0.5489 bits/point +EBPFP 0.5489 equivalent bits/point +MSE 655.606653 +---------------------- ---------------------------------------------------------- +Time: 66.570s Load: 1.221s, Pack+Encode: 33.568s, Decode+Unpack: 31.782s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 655.6067 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.5337 bits/point +Avg EBPFP 0.5337 equivalent bits/point +Avg MSE 610.669646 +Avg Time 66.466s +------------------------ ---------------------------- diff --git a/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..2e095c33e40e8e6e07fd0f8371fb0c8634c246e6 --- /dev/null +++ b/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.195s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 494,788B, BPFP=0.3141 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 493,116B, BPFP=0.3130 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,033,520B, BPFP=0.6560 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,044,008B, BPFP=0.6627 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,348,772B, BPFP=0.8561 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,336,720B, BPFP=0.8485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 978,548B, BPFP=0.6211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,041,544B, BPFP=0.6611 +⌛️ [2/4] FRONTEND: Frontend time: 35.013s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.915s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561056 4.26302348 + layer.9.1 0.14522085 4.22326402 + layer.19.0 3.25142184 9.13219108 + layer.19.1 3.25206135 7.19586473 + layer.29.0 4.23946030 42.75620024 + layer.29.1 4.24539299 37.06357654 + layer.39.0 32.17105490 3071.76535587 + layer.39.1 19.15684032 4016.73188170 + ------------------------------------------------------------------------------------- + TOTAL 8.32588289 899.14141971 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7771016 +BPFP 0.6166 bits/point +EBPFP 0.6166 equivalent bits/point +MSE 899.141420 +---------------------- ---------------------------------------------------------- +Time: 68.123s Load: 1.195s, Pack+Encode: 35.013s, Decode+Unpack: 31.915s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 899.1414 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.162s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 596,936B, BPFP=0.3789 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 589,592B, BPFP=0.3742 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,274,584B, BPFP=0.8090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,254,840B, BPFP=0.7965 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,633,896B, BPFP=1.0371 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,611,348B, BPFP=1.0228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,200,112B, BPFP=0.7618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,195,716B, BPFP=0.7590 +⌛️ [2/4] FRONTEND: Frontend time: 32.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.942s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03307773 4.33239115 + layer.9.1 0.03291117 0.32820091 + layer.19.0 0.04156009 8.21578445 + layer.19.1 0.03760627 11.95337001 + layer.29.0 4.28582750 59.24165685 + layer.29.1 4.28551552 35.03167147 + layer.39.0 9.83402183 3232.62138447 + layer.39.1 9.85397836 3544.16412090 + ------------------------------------------------------------------------------------- + TOTAL 3.55056231 861.98607253 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9357024 +BPFP 0.7424 bits/point +EBPFP 0.7424 equivalent bits/point +MSE 861.986073 +---------------------- ---------------------------------------------------------- +Time: 65.621s Load: 1.162s, Pack+Encode: 32.516s, Decode+Unpack: 31.942s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 861.9861 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.124s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 652,136B, BPFP=0.4139 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 651,120B, BPFP=0.4133 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,295,280B, BPFP=0.8222 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,300,068B, BPFP=0.8252 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,707,392B, BPFP=1.0838 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,716,808B, BPFP=1.0897 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,441,628B, BPFP=0.9151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,454,848B, BPFP=0.9235 +⌛️ [2/4] FRONTEND: Frontend time: 33.670s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.821s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083900 0.34123290 + layer.9.1 0.00259629 0.34626329 + layer.19.0 0.00955961 7.72141976 + layer.19.1 0.08538111 7.22721693 + layer.29.0 0.11631418 104.92980175 + layer.29.1 0.11200302 95.79619963 + layer.39.0 14.47657393 5332.76795580 + layer.39.1 13.08093694 5974.30289243 + ------------------------------------------------------------------------------------- + TOTAL 3.48552551 1440.42912281 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10219280 +BPFP 0.8108 bits/point +EBPFP 0.8108 equivalent bits/point +MSE 1440.429123 +---------------------- ---------------------------------------------------------- +Time: 66.615s Load: 1.124s, Pack+Encode: 33.670s, Decode+Unpack: 31.821s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1440.4291 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.027s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 519,088B, BPFP=0.3295 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 516,980B, BPFP=0.3282 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,101,060B, BPFP=0.6989 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,101,496B, BPFP=0.6992 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,381,952B, BPFP=0.8772 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,385,300B, BPFP=0.8793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 978,136B, BPFP=0.6209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,008,524B, BPFP=0.6402 +⌛️ [2/4] FRONTEND: Frontend time: 33.075s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.70169554 2.99917705 + layer.9.1 0.03294074 4.31750881 + layer.19.0 3.25671692 8.00310647 + layer.19.1 3.25834093 10.44800130 + layer.29.0 0.10810242 56.31226641 + layer.29.1 0.10661203 26.91523298 + layer.39.0 8.95005916 2225.29005525 + layer.39.1 8.98756017 2076.66753331 + ------------------------------------------------------------------------------------- + TOTAL 3.42525349 551.36911020 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7992536 +BPFP 0.6342 bits/point +EBPFP 0.6342 equivalent bits/point +MSE 551.369110 +---------------------- ---------------------------------------------------------- +Time: 65.643s Load: 1.027s, Pack+Encode: 33.075s, Decode+Unpack: 31.541s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 551.3691 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.982s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 610,264B, BPFP=0.3874 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 609,792B, BPFP=0.3871 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,226,364B, BPFP=0.7784 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,229,452B, BPFP=0.7804 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,648,548B, BPFP=1.0464 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,663,300B, BPFP=1.0558 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,258,652B, BPFP=0.7989 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,277,380B, BPFP=0.8108 +⌛️ [2/4] FRONTEND: Frontend time: 33.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527870 4.25935589 + layer.9.1 0.14521496 0.33607127 + layer.19.0 0.03964342 12.38608781 + layer.19.1 0.03956446 12.87063927 + layer.29.0 0.12258449 71.22338621 + layer.29.1 0.12735008 120.35069670 + layer.39.0 32.94776263 4959.68833279 + layer.39.1 29.25669534 4572.41988950 + ------------------------------------------------------------------------------------- + TOTAL 7.85301176 1219.19180743 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9523752 +BPFP 0.7556 bits/point +EBPFP 0.7556 equivalent bits/point +MSE 1219.191807 +---------------------- ---------------------------------------------------------- +Time: 66.181s Load: 0.982s, Pack+Encode: 33.592s, Decode+Unpack: 31.607s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1219.1918 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.924s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 545,680B, BPFP=0.3464 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 548,584B, BPFP=0.3482 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,113,812B, BPFP=0.7070 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,109,196B, BPFP=0.7041 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,461,824B, BPFP=0.9279 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,483,492B, BPFP=0.9416 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,087,488B, BPFP=0.6903 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,112,592B, BPFP=0.7062 +⌛️ [2/4] FRONTEND: Frontend time: 32.729s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.688s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66816044 4.38616874 + layer.9.1 2.66817504 4.39316781 + layer.19.0 3.22262959 8.45248073 + layer.19.1 3.22037432 7.08999228 + layer.29.0 4.30448692 94.94080070 + layer.29.1 4.31085282 76.78136172 + layer.39.0 38.33931691 3228.28664283 + layer.39.1 57.25219370 3000.18102047 + ------------------------------------------------------------------------------------- + TOTAL 14.49827372 803.06395441 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8462668 +BPFP 0.6715 bits/point +EBPFP 0.6715 equivalent bits/point +MSE 803.063954 +---------------------- ---------------------------------------------------------- +Time: 65.341s Load: 0.924s, Pack+Encode: 32.729s, Decode+Unpack: 31.688s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 803.0640 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,744B, BPFP=0.3832 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 606,932B, BPFP=0.3852 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,171,544B, BPFP=0.7436 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,172,324B, BPFP=0.7441 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,469,952B, BPFP=0.9331 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,464,248B, BPFP=0.9294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,007,556B, BPFP=0.6395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,027,792B, BPFP=0.6524 +⌛️ [2/4] FRONTEND: Frontend time: 32.986s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.892s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332920 0.45257908 + layer.9.1 0.00092169 0.33425708 + layer.19.0 3.23006092 13.20551166 + layer.19.1 3.23257961 14.38979983 + layer.29.0 4.28548854 38.25077186 + layer.29.1 4.27808990 66.83619902 + layer.39.0 10.57841825 2162.03396165 + layer.39.1 20.33118703 2230.59896003 + ------------------------------------------------------------------------------------- + TOTAL 5.75625939 565.76275502 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8524092 +BPFP 0.6763 bits/point +EBPFP 0.6763 equivalent bits/point +MSE 565.762755 +---------------------- ---------------------------------------------------------- +Time: 66.053s Load: 1.175s, Pack+Encode: 32.986s, Decode+Unpack: 31.892s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.7628 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.162s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 654,528B, BPFP=0.4155 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 655,416B, BPFP=0.4160 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,187,524B, BPFP=0.7538 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,200,720B, BPFP=0.7622 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,430,064B, BPFP=0.9077 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,443,016B, BPFP=0.9160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 924,940B, BPFP=0.5871 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 955,384B, BPFP=0.6064 +⌛️ [2/4] FRONTEND: Frontend time: 32.443s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.154s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14443954 12.63663750 + layer.9.1 0.14435121 0.62012008 + layer.19.0 0.03807715 13.38050455 + layer.19.1 0.03781311 14.97812779 + layer.29.0 0.10781899 35.23161257 + layer.29.1 0.10618912 46.25140153 + layer.39.0 9.30898666 2188.51722457 + layer.39.1 9.83625107 2300.70376991 + ------------------------------------------------------------------------------------- + TOTAL 2.46549086 576.53992481 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8451592 +BPFP 0.6706 bits/point +EBPFP 0.6706 equivalent bits/point +MSE 576.539925 +---------------------- ---------------------------------------------------------- +Time: 64.759s Load: 1.162s, Pack+Encode: 32.443s, Decode+Unpack: 31.154s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 576.5399 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.135s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,320B, BPFP=0.4293 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 674,924B, BPFP=0.4284 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,299,432B, BPFP=0.8248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,298,596B, BPFP=0.8243 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,714,940B, BPFP=1.0886 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,692,296B, BPFP=1.0742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,126,676B, BPFP=0.7152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,095,468B, BPFP=0.6953 +⌛️ [2/4] FRONTEND: Frontend time: 32.908s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.577s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14543928 3.04270120 + layer.9.1 0.14562574 4.48078422 + layer.19.0 0.11552505 26.61147475 + layer.19.1 0.12052174 8.01896442 + layer.29.0 0.10841144 49.40936789 + layer.29.1 0.10845811 55.17433275 + layer.39.0 9.17501701 1911.96945076 + layer.39.1 9.20635778 1880.63405915 + ------------------------------------------------------------------------------------- + TOTAL 2.39066952 492.41764189 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9578652 +BPFP 0.7600 bits/point +EBPFP 0.7600 equivalent bits/point +MSE 492.417642 +---------------------- ---------------------------------------------------------- +Time: 65.620s Load: 1.135s, Pack+Encode: 32.908s, Decode+Unpack: 31.577s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 492.4176 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.065s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 587,500B, BPFP=0.3729 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 588,240B, BPFP=0.3734 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,044,032B, BPFP=0.6627 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,045,680B, BPFP=0.6637 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,305,928B, BPFP=0.8289 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,328,604B, BPFP=0.8433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 914,740B, BPFP=0.5806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 963,124B, BPFP=0.6113 +⌛️ [2/4] FRONTEND: Frontend time: 32.811s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.78226592 4.39547195 + layer.9.1 2.78427046 0.47291269 + layer.19.0 3.22580366 14.69494181 + layer.19.1 3.22969594 5.71392336 + layer.29.0 4.29525448 28.42928380 + layer.29.1 0.11349234 32.76394164 + layer.39.0 8.89338553 1933.09879753 + layer.39.1 8.88767087 2194.44312642 + ------------------------------------------------------------------------------------- + TOTAL 4.27647990 526.75154990 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7777848 +BPFP 0.6171 bits/point +EBPFP 0.6171 equivalent bits/point +MSE 526.751550 +---------------------- ---------------------------------------------------------- +Time: 65.333s Load: 1.065s, Pack+Encode: 32.811s, Decode+Unpack: 31.457s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 526.7515 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.046s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 583,992B, BPFP=0.3707 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 578,572B, BPFP=0.3672 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,138,684B, BPFP=0.7228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,125,672B, BPFP=0.7145 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,416,308B, BPFP=0.8990 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,395,196B, BPFP=0.8856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 912,140B, BPFP=0.5790 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 903,348B, BPFP=0.5734 +⌛️ [2/4] FRONTEND: Frontend time: 33.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14514284 12.72940110 + layer.9.1 0.14518188 4.33558077 + layer.19.0 0.04057091 33.90577775 + layer.19.1 0.04041447 9.82529338 + layer.29.0 4.25641542 37.35819437 + layer.29.1 4.26613502 45.23272465 + layer.39.0 12.58558458 1991.55654859 + layer.39.1 8.96866240 1810.25479363 + ------------------------------------------------------------------------------------- + TOTAL 3.80601344 493.14978928 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8053912 +BPFP 0.6390 bits/point +EBPFP 0.6390 equivalent bits/point +MSE 493.149789 +---------------------- ---------------------------------------------------------- +Time: 66.003s Load: 1.046s, Pack+Encode: 33.505s, Decode+Unpack: 31.453s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 493.1498 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.989s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 582,040B, BPFP=0.3694 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 582,052B, BPFP=0.3695 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,137,408B, BPFP=0.7220 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,130,436B, BPFP=0.7175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,388,352B, BPFP=0.8813 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,387,304B, BPFP=0.8806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 904,684B, BPFP=0.5742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 925,640B, BPFP=0.5875 +⌛️ [2/4] FRONTEND: Frontend time: 33.051s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.204s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67015388 3.06126605 + layer.9.1 0.00076871 0.32895531 + layer.19.0 3.22151687 8.51876193 + layer.19.1 3.22388957 9.00811083 + layer.29.0 4.24084786 32.85252986 + layer.29.1 4.24602234 32.35736919 + layer.39.0 7.87160790 2092.55102372 + layer.39.1 9.85764150 2270.32759181 + ------------------------------------------------------------------------------------- + TOTAL 4.41655608 556.12570109 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8037916 +BPFP 0.6378 bits/point +EBPFP 0.6378 equivalent bits/point +MSE 556.125701 +---------------------- ---------------------------------------------------------- +Time: 65.244s Load: 0.989s, Pack+Encode: 33.051s, Decode+Unpack: 31.204s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 556.1257 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.927s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 669,532B, BPFP=0.4250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 671,172B, BPFP=0.4260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,285,248B, BPFP=0.8158 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,291,580B, BPFP=0.8198 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,737,828B, BPFP=1.1031 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,741,024B, BPFP=1.1051 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,493,860B, BPFP=0.9482 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,517,908B, BPFP=0.9635 +⌛️ [2/4] FRONTEND: Frontend time: 32.798s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.576s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00068237 0.34078211 + layer.9.1 0.00070576 4.30522418 + layer.19.0 0.00823322 37.92533819 + layer.19.1 0.08594799 22.66934362 + layer.29.0 0.12200666 53.32759181 + layer.29.1 0.12451052 53.43816014 + layer.39.0 55.99513528 4676.47611310 + layer.39.1 28.81185256 4670.58628534 + ------------------------------------------------------------------------------------- + TOTAL 10.64363429 1189.88360481 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10408152 +BPFP 0.8258 bits/point +EBPFP 0.8258 equivalent bits/point +MSE 1189.883605 +---------------------- ---------------------------------------------------------- +Time: 65.301s Load: 0.927s, Pack+Encode: 32.798s, Decode+Unpack: 31.576s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1189.8836 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.924s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 635,608B, BPFP=0.4035 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 634,500B, BPFP=0.4027 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,164,648B, BPFP=0.7393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,161,956B, BPFP=0.7376 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,424,160B, BPFP=0.9040 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,414,244B, BPFP=0.8977 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 976,132B, BPFP=0.6196 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 990,764B, BPFP=0.6289 +⌛️ [2/4] FRONTEND: Frontend time: 33.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.745s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14556937 0.48073427 + layer.9.1 0.03327741 0.35628583 + layer.19.0 0.11590617 14.15910764 + layer.19.1 0.11733878 34.45978226 + layer.29.0 0.11334742 60.31355115 + layer.29.1 4.29039579 44.17287537 + layer.39.0 9.10722066 2125.34221644 + layer.39.1 44.52401893 2250.55606110 + ------------------------------------------------------------------------------------- + TOTAL 7.30588432 566.23007676 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8402012 +BPFP 0.6666 bits/point +EBPFP 0.6666 equivalent bits/point +MSE 566.230077 +---------------------- ---------------------------------------------------------- +Time: 66.276s Load: 0.924s, Pack+Encode: 33.607s, Decode+Unpack: 31.745s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 566.2301 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.983s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 731,792B, BPFP=0.4645 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 734,660B, BPFP=0.4663 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,339,976B, BPFP=0.8505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,321,708B, BPFP=0.8390 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,631,244B, BPFP=1.0354 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,650,060B, BPFP=1.0474 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,021,712B, BPFP=0.6485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,044,352B, BPFP=0.6629 +⌛️ [2/4] FRONTEND: Frontend time: 32.830s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.621s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11318631 0.35092279 + layer.9.1 0.11319129 4.41872442 + layer.19.0 0.00665199 18.36065846 + layer.19.1 0.00853768 4.59876135 + layer.29.0 4.27225940 35.95250548 + layer.29.1 4.27324961 39.46602565 + layer.39.0 14.80262837 4276.09749756 + layer.39.1 16.56649765 4100.07669808 + ------------------------------------------------------------------------------------- + TOTAL 5.01952529 1059.91522423 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9475504 +BPFP 0.7518 bits/point +EBPFP 0.7518 equivalent bits/point +MSE 1059.915224 +---------------------- ---------------------------------------------------------- +Time: 65.433s Load: 0.983s, Pack+Encode: 32.830s, Decode+Unpack: 31.621s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1059.9152 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.963s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,300B, BPFP=0.4007 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 631,752B, BPFP=0.4010 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,181,516B, BPFP=0.7500 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,180,140B, BPFP=0.7491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,516,976B, BPFP=0.9629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,508,632B, BPFP=0.9576 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,051,744B, BPFP=0.6676 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,068,348B, BPFP=0.6781 +⌛️ [2/4] FRONTEND: Frontend time: 33.241s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.576s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00065117 3.06475400 + layer.9.1 0.00066201 0.59188991 + layer.19.0 0.00984582 23.28936972 + layer.19.1 0.01156107 7.26079519 + layer.29.0 4.26547583 33.95255880 + layer.29.1 4.26296603 39.33037963 + layer.39.0 11.21169412 2651.03152421 + layer.39.1 9.31977106 2466.01039974 + ------------------------------------------------------------------------------------- + TOTAL 3.63532839 653.06645890 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8770408 +BPFP 0.6959 bits/point +EBPFP 0.6959 equivalent bits/point +MSE 653.066459 +---------------------- ---------------------------------------------------------- +Time: 65.779s Load: 0.963s, Pack+Encode: 33.241s, Decode+Unpack: 31.576s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 653.0665 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.869s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 589,808B, BPFP=0.3744 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 590,372B, BPFP=0.3747 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,087,236B, BPFP=0.6901 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,083,476B, BPFP=0.6877 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,368,072B, BPFP=0.8684 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,358,124B, BPFP=0.8621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 977,864B, BPFP=0.6207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 949,252B, BPFP=0.6025 +⌛️ [2/4] FRONTEND: Frontend time: 32.750s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.713s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00085355 4.34842652 + layer.9.1 0.00085581 4.36231326 + layer.19.0 0.00808159 6.33577754 + layer.19.1 0.00635426 8.60895099 + layer.29.0 4.24551200 33.39364641 + layer.29.1 4.24803037 58.07482938 + layer.39.0 9.19283951 2434.02291193 + layer.39.1 9.46657027 2381.31784205 + ------------------------------------------------------------------------------------- + TOTAL 3.39613717 616.30808726 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8004204 +BPFP 0.6351 bits/point +EBPFP 0.6351 equivalent bits/point +MSE 616.308087 +---------------------- ---------------------------------------------------------- +Time: 65.331s Load: 0.869s, Pack+Encode: 32.750s, Decode+Unpack: 31.713s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 616.3081 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.924s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 589,300B, BPFP=0.3741 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 588,508B, BPFP=0.3736 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,224,456B, BPFP=0.7772 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,225,488B, BPFP=0.7779 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,588,452B, BPFP=1.0083 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,595,456B, BPFP=1.0127 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,023,656B, BPFP=0.6498 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,046,540B, BPFP=0.6643 +⌛️ [2/4] FRONTEND: Frontend time: 32.368s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.818s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67056234 4.50424394 + layer.9.1 2.67147828 0.33683174 + layer.19.0 0.00618387 10.99951886 + layer.19.1 0.08383032 6.79058273 + layer.29.0 4.28489822 76.77801024 + layer.29.1 4.28470970 54.78017346 + layer.39.0 10.15376305 1842.40786480 + layer.39.1 8.47863686 1980.93223919 + ------------------------------------------------------------------------------------- + TOTAL 4.07925783 497.19118312 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8881856 +BPFP 0.7047 bits/point +EBPFP 0.7047 equivalent bits/point +MSE 497.191183 +---------------------- ---------------------------------------------------------- +Time: 65.110s Load: 0.924s, Pack+Encode: 32.368s, Decode+Unpack: 31.818s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 497.1912 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.875s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 579,660B, BPFP=0.3679 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 584,120B, BPFP=0.3708 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,205,080B, BPFP=0.7649 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,209,676B, BPFP=0.7678 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,601,072B, BPFP=1.0163 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,624,496B, BPFP=1.0311 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,096,900B, BPFP=0.6963 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,135,404B, BPFP=0.7207 +⌛️ [2/4] FRONTEND: Frontend time: 33.377s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67132404 0.32998031 + layer.9.1 2.67117709 3.02714920 + layer.19.0 0.00597838 12.57365763 + layer.19.1 0.00605309 3.12998723 + layer.29.0 4.29273040 74.66793955 + layer.29.1 4.29206328 31.64563445 + layer.39.0 9.96127074 3067.62333442 + layer.39.1 10.21295854 3349.04582385 + ------------------------------------------------------------------------------------- + TOTAL 4.26419445 817.75543833 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9036408 +BPFP 0.7170 bits/point +EBPFP 0.7170 equivalent bits/point +MSE 817.755438 +---------------------- ---------------------------------------------------------- +Time: 65.862s Load: 0.875s, Pack+Encode: 33.377s, Decode+Unpack: 31.610s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 817.7554 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.873s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 585,884B, BPFP=0.3719 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 588,132B, BPFP=0.3733 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,108,556B, BPFP=0.7037 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,117,908B, BPFP=0.7096 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,312,552B, BPFP=0.8331 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,322,048B, BPFP=0.8392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 908,148B, BPFP=0.5764 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 925,096B, BPFP=0.5872 +⌛️ [2/4] FRONTEND: Frontend time: 32.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.693s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568686 4.34939007 + layer.9.1 0.14558674 11.37770023 + layer.19.0 0.00960369 8.01391625 + layer.19.1 0.03847206 10.69505606 + layer.29.0 4.24438723 38.74754733 + layer.29.1 4.24578970 33.95905102 + layer.39.0 9.23757985 2481.08303542 + layer.39.1 9.43674592 2468.31540461 + ------------------------------------------------------------------------------------- + TOTAL 3.43798151 632.06763762 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7868324 +BPFP 0.6243 bits/point +EBPFP 0.6243 equivalent bits/point +MSE 632.067638 +---------------------- ---------------------------------------------------------- +Time: 65.154s Load: 0.873s, Pack+Encode: 32.587s, Decode+Unpack: 31.693s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 632.0676 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.872s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 675,316B, BPFP=0.4287 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 671,404B, BPFP=0.4262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,323,900B, BPFP=0.8403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,324,604B, BPFP=0.8408 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,667,976B, BPFP=1.0587 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,651,272B, BPFP=1.0481 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,106,772B, BPFP=0.7025 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,094,620B, BPFP=0.6948 +⌛️ [2/4] FRONTEND: Frontend time: 32.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11307967 4.40338157 + layer.9.1 0.00073224 4.29075919 + layer.19.0 0.08207503 12.61029158 + layer.19.1 0.08214869 17.13200637 + layer.29.0 4.26728487 46.11347294 + layer.29.1 4.26774951 41.25813749 + layer.39.0 12.81553410 2565.66997075 + layer.39.1 23.05196315 2777.28111797 + ------------------------------------------------------------------------------------- + TOTAL 5.58507091 683.59489223 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9515864 +BPFP 0.7550 bits/point +EBPFP 0.7550 equivalent bits/point +MSE 683.594892 +---------------------- ---------------------------------------------------------- +Time: 65.061s Load: 0.872s, Pack+Encode: 32.578s, Decode+Unpack: 31.610s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 683.5949 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.879s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 855,784B, BPFP=0.5432 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 857,292B, BPFP=0.5442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,680,224B, BPFP=1.0665 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,677,572B, BPFP=1.0648 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,175,056B, BPFP=1.3806 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,183,588B, BPFP=1.3860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,275,048B, BPFP=0.8093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,247,240B, BPFP=0.7917 +⌛️ [2/4] FRONTEND: Frontend time: 33.314s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.755s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519844 5.13032841 + layer.9.1 0.14499054 4.71952471 + layer.19.0 0.12156012 265.17809555 + layer.19.1 0.12030756 240.66925983 + layer.29.0 0.12020218 84.02188617 + layer.29.1 0.12115470 75.38345487 + layer.39.0 8.85439666 2698.41290218 + layer.39.1 8.75438231 2669.46100097 + ------------------------------------------------------------------------------------- + TOTAL 2.29777406 755.37205659 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 11951804 +BPFP 0.9483 bits/point +EBPFP 0.9483 equivalent bits/point +MSE 755.372057 +---------------------- ---------------------------------------------------------- +Time: 65.948s Load: 0.879s, Pack+Encode: 33.314s, Decode+Unpack: 31.755s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 755.3721 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.878s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 873,116B, BPFP=0.5542 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 872,032B, BPFP=0.5535 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,794,392B, BPFP=1.1390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,828,028B, BPFP=1.1603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,173,556B, BPFP=1.3797 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,182,708B, BPFP=1.3855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,166,400B, BPFP=0.7404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,169,384B, BPFP=0.7423 +⌛️ [2/4] FRONTEND: Frontend time: 33.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.734s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14541288 25.04470669 + layer.9.1 0.14479464 13.33225976 + layer.19.0 0.11855170 230.99912658 + layer.19.1 0.11778439 222.00688577 + layer.29.0 0.12648388 76.78391087 + layer.29.1 0.12520221 148.02802039 + layer.39.0 8.37129624 2621.33636659 + layer.39.1 8.45478741 2471.70604485 + ------------------------------------------------------------------------------------- + TOTAL 2.20053917 726.15466519 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 12059616 +BPFP 0.9569 bits/point +EBPFP 0.9569 equivalent bits/point +MSE 726.154665 +---------------------- ---------------------------------------------------------- +Time: 66.195s Load: 0.878s, Pack+Encode: 33.583s, Decode+Unpack: 31.734s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 726.1547 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.880s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 877,960B, BPFP=0.5573 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 878,812B, BPFP=0.5578 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,758,840B, BPFP=1.1164 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,729,884B, BPFP=1.0980 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,338,708B, BPFP=1.4845 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,330,564B, BPFP=1.4793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,353,168B, BPFP=0.8589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,369,428B, BPFP=0.8692 +⌛️ [2/4] FRONTEND: Frontend time: 33.751s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.673s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14458197 12.44604119 + layer.9.1 0.14461228 4.22725120 + layer.19.0 0.12127609 233.07915583 + layer.19.1 0.12505172 181.02906646 + layer.29.0 0.11568762 65.58387837 + layer.29.1 0.11796058 43.78357065 + layer.39.0 8.63782956 2637.27575561 + layer.39.1 8.69862780 2506.60724732 + ------------------------------------------------------------------------------------- + TOTAL 2.26320345 710.50399583 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 12637364 +BPFP 1.0027 bits/point +EBPFP 1.0027 equivalent bits/point +MSE 710.503996 +---------------------- ---------------------------------------------------------- +Time: 66.304s Load: 0.880s, Pack+Encode: 33.751s, Decode+Unpack: 31.673s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 710.5040 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.818s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 875,612B, BPFP=0.5558 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 876,608B, BPFP=0.5564 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,869,928B, BPFP=1.1869 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,842,124B, BPFP=1.1693 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,361,800B, BPFP=1.4992 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,338,664B, BPFP=1.4845 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,453,428B, BPFP=0.9226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,454,664B, BPFP=0.9233 +⌛️ [2/4] FRONTEND: Frontend time: 33.232s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.921s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14478087 4.57212661 + layer.9.1 0.14472154 0.51175080 + layer.19.0 0.13423899 230.26781362 + layer.19.1 0.13534726 469.86045661 + layer.29.0 0.11251127 56.05172449 + layer.29.1 0.11242151 74.59256175 + layer.39.0 10.58490794 2744.73253169 + layer.39.1 8.80008176 2737.55411115 + ------------------------------------------------------------------------------------- + TOTAL 2.52112639 789.76788459 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 13072828 +BPFP 1.0372 bits/point +EBPFP 1.0372 equivalent bits/point +MSE 789.767885 +---------------------- ---------------------------------------------------------- +Time: 65.972s Load: 0.818s, Pack+Encode: 33.232s, Decode+Unpack: 31.921s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 789.7679 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.819s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 807,216B, BPFP=0.5124 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 808,944B, BPFP=0.5135 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,445,964B, BPFP=0.9178 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,446,056B, BPFP=0.9179 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,973,952B, BPFP=1.2530 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,979,148B, BPFP=1.2563 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,367,716B, BPFP=0.8682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,396,172B, BPFP=0.8862 +⌛️ [2/4] FRONTEND: Frontend time: 32.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.736s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14590075 0.50786185 + layer.9.1 0.14620647 8.22858481 + layer.19.0 0.11628058 59.59027157 + layer.19.1 0.11601873 83.59315080 + layer.29.0 0.11558260 54.68199037 + layer.29.1 0.11828149 79.33484319 + layer.39.0 28.43028163 3460.34254144 + layer.39.1 24.81181701 3220.65908352 + ------------------------------------------------------------------------------------- + TOTAL 6.75004616 870.86729094 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 11225168 +BPFP 0.8906 bits/point +EBPFP 0.8906 equivalent bits/point +MSE 870.867291 +---------------------- ---------------------------------------------------------- +Time: 65.061s Load: 0.819s, Pack+Encode: 32.505s, Decode+Unpack: 31.736s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 870.8673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.183s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 748,944B, BPFP=0.4754 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 750,896B, BPFP=0.4766 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,498,264B, BPFP=0.9510 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,503,632B, BPFP=0.9544 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,041,956B, BPFP=1.2961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,038,904B, BPFP=1.2942 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,336,652B, BPFP=0.8484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,314,756B, BPFP=0.8345 +⌛️ [2/4] FRONTEND: Frontend time: 32.894s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.826s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11529700 4.31613489 + layer.9.1 0.14629077 0.35552528 + layer.19.0 0.09721754 45.85351499 + layer.19.1 0.12446257 52.16028701 + layer.29.0 4.28687864 128.91664974 + layer.29.1 4.28715508 118.38708564 + layer.39.0 11.34089363 3057.54111147 + layer.39.1 19.75513766 2871.09132272 + ------------------------------------------------------------------------------------- + TOTAL 5.01916661 784.82770397 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 11234004 +BPFP 0.8913 bits/point +EBPFP 0.8913 equivalent bits/point +MSE 784.827704 +---------------------- ---------------------------------------------------------- +Time: 65.903s Load: 1.183s, Pack+Encode: 32.894s, Decode+Unpack: 31.826s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 784.8277 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.168s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 720,452B, BPFP=0.4573 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 717,580B, BPFP=0.4555 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,373,872B, BPFP=0.8721 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,381,064B, BPFP=0.8766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,856,020B, BPFP=1.1781 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,846,392B, BPFP=1.1720 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,489,464B, BPFP=0.9454 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,526,872B, BPFP=0.9692 +⌛️ [2/4] FRONTEND: Frontend time: 32.822s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.428s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14538718 3.03115891 + layer.9.1 0.14538559 0.47887426 + layer.19.0 0.11434236 37.44751127 + layer.19.1 0.11406084 74.36721543 + layer.29.0 0.11219077 52.20099732 + layer.29.1 0.11281304 42.62871202 + layer.39.0 79.88316542 3776.58303542 + layer.39.1 46.71980622 3913.66265843 + ------------------------------------------------------------------------------------- + TOTAL 15.91839393 987.55002038 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10911716 +BPFP 0.8658 bits/point +EBPFP 0.8658 equivalent bits/point +MSE 987.550020 +---------------------- ---------------------------------------------------------- +Time: 65.419s Load: 1.168s, Pack+Encode: 32.822s, Decode+Unpack: 31.428s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 987.5500 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 803,128B, BPFP=0.5098 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 801,720B, BPFP=0.5089 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,426,416B, BPFP=0.9054 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,442,304B, BPFP=0.9155 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,975,508B, BPFP=1.2540 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,971,228B, BPFP=1.2512 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,515,096B, BPFP=0.9617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,524,868B, BPFP=0.9679 +⌛️ [2/4] FRONTEND: Frontend time: 33.000s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.805s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14512126 4.31895033 + layer.9.1 0.14517278 0.65110631 + layer.19.0 0.11689420 111.09168833 + layer.19.1 0.12099910 89.73844248 + layer.29.0 0.11847120 38.66708899 + layer.29.1 0.12399357 63.73400938 + layer.39.0 75.86630139 3457.83555411 + layer.39.1 56.61936342 3649.89697758 + ------------------------------------------------------------------------------------- + TOTAL 16.65703962 926.99172719 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 11460268 +BPFP 0.9093 bits/point +EBPFP 0.9093 equivalent bits/point +MSE 926.991727 +---------------------- ---------------------------------------------------------- +Time: 65.887s Load: 1.082s, Pack+Encode: 33.000s, Decode+Unpack: 31.805s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 926.9917 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 733,444B, BPFP=0.4656 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 729,456B, BPFP=0.4630 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,368,716B, BPFP=0.8688 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,362,616B, BPFP=0.8649 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,786,868B, BPFP=1.1342 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,788,368B, BPFP=1.1352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,491,872B, BPFP=0.9470 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,488,260B, BPFP=0.9447 +⌛️ [2/4] FRONTEND: Frontend time: 33.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488649 4.31972060 + layer.9.1 0.14606862 0.35774060 + layer.19.0 0.08767178 13.22489692 + layer.19.1 0.11443626 50.75284368 + layer.29.0 0.10933029 41.87274029 + layer.29.1 0.10817130 45.79257394 + layer.39.0 52.66717785 3484.87877803 + layer.39.1 62.91127214 3467.98667533 + ------------------------------------------------------------------------------------- + TOTAL 14.53237684 888.64824617 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10749600 +BPFP 0.8529 bits/point +EBPFP 0.8529 equivalent bits/point +MSE 888.648246 +---------------------- ---------------------------------------------------------- +Time: 65.948s Load: 1.060s, Pack+Encode: 33.289s, Decode+Unpack: 31.599s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 888.6482 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.973s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 756,084B, BPFP=0.4799 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 757,488B, BPFP=0.4808 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,362,944B, BPFP=0.8651 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,354,284B, BPFP=0.8596 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,793,092B, BPFP=1.1382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,775,848B, BPFP=1.1272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,345,020B, BPFP=0.8538 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,330,248B, BPFP=0.8444 +⌛️ [2/4] FRONTEND: Frontend time: 32.968s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.660s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509552 4.23336099 + layer.9.1 0.14520687 4.74500960 + layer.19.0 0.12118574 36.78574657 + layer.19.1 0.11709642 46.42897912 + layer.29.0 0.10963326 71.93417391 + layer.29.1 0.10842036 60.85848635 + layer.39.0 53.79489966 3138.64998375 + layer.39.1 62.27410526 2922.54468638 + ------------------------------------------------------------------------------------- + TOTAL 14.60195539 785.77255333 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10475008 +BPFP 0.8311 bits/point +EBPFP 0.8311 equivalent bits/point +MSE 785.772553 +---------------------- ---------------------------------------------------------- +Time: 65.601s Load: 0.973s, Pack+Encode: 32.968s, Decode+Unpack: 31.660s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 785.7726 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.933s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 892,752B, BPFP=0.5667 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 889,872B, BPFP=0.5648 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,533,404B, BPFP=0.9733 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,538,476B, BPFP=0.9765 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,963,340B, BPFP=1.2462 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,946,164B, BPFP=1.2353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,523,980B, BPFP=0.9673 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,521,688B, BPFP=0.9659 +⌛️ [2/4] FRONTEND: Frontend time: 33.734s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14577075 4.91060470 + layer.9.1 0.14541274 0.52390210 + layer.19.0 0.13069581 86.67371831 + layer.19.1 0.13545482 190.23127234 + layer.29.0 0.11331055 77.51643240 + layer.29.1 0.11244963 71.90223330 + layer.39.0 32.27446072 3491.91257719 + layer.39.1 16.59366367 3596.35521612 + ------------------------------------------------------------------------------------- + TOTAL 6.20640234 940.00324456 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 11809676 +BPFP 0.9370 bits/point +EBPFP 0.9370 equivalent bits/point +MSE 940.003245 +---------------------- ---------------------------------------------------------- +Time: 66.736s Load: 0.933s, Pack+Encode: 33.734s, Decode+Unpack: 32.068s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 940.0032 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.128s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 704,408B, BPFP=0.4471 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 701,724B, BPFP=0.4454 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,308,224B, BPFP=0.8304 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,310,508B, BPFP=0.8318 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,804,940B, BPFP=1.1457 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,806,084B, BPFP=1.1464 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,277,360B, BPFP=0.8108 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,255,700B, BPFP=0.7971 +⌛️ [2/4] FRONTEND: Frontend time: 32.932s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.843s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585920 4.35497841 + layer.9.1 0.14576220 4.34766609 + layer.19.0 0.12270736 149.06286562 + layer.19.1 0.12453605 121.93992728 + layer.29.0 0.11393550 67.23906199 + layer.29.1 0.11678154 52.55000812 + layer.39.0 53.83016636 3255.87000325 + layer.39.1 40.65720720 2962.16347091 + ------------------------------------------------------------------------------------- + TOTAL 11.90711942 827.19099771 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10168948 +BPFP 0.8068 bits/point +EBPFP 0.8068 equivalent bits/point +MSE 827.190998 +---------------------- ---------------------------------------------------------- +Time: 65.903s Load: 1.128s, Pack+Encode: 32.932s, Decode+Unpack: 31.843s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 827.1910 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 687,436B, BPFP=0.4363 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 684,260B, BPFP=0.4343 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,395,692B, BPFP=0.8859 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,398,204B, BPFP=0.8875 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,806,912B, BPFP=1.1469 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,801,204B, BPFP=1.1433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,213,076B, BPFP=0.7700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,208,716B, BPFP=0.7672 +⌛️ [2/4] FRONTEND: Frontend time: 33.411s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.560s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568599 4.28813386 + layer.9.1 0.03329684 0.35063767 + layer.19.0 0.11848472 3.75588064 + layer.19.1 0.11973745 7.97759143 + layer.29.0 0.10886538 32.01597284 + layer.29.1 0.10946879 56.63115961 + layer.39.0 14.08931437 2797.99837504 + layer.39.1 9.95616799 2528.68995775 + ------------------------------------------------------------------------------------- + TOTAL 3.08512769 678.96346361 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10195500 +BPFP 0.8089 bits/point +EBPFP 0.8089 equivalent bits/point +MSE 678.963464 +---------------------- ---------------------------------------------------------- +Time: 66.039s Load: 1.068s, Pack+Encode: 33.411s, Decode+Unpack: 31.560s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.9635 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.003s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 721,812B, BPFP=0.4582 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 721,056B, BPFP=0.4577 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,363,556B, BPFP=0.8655 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,373,308B, BPFP=0.8717 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,827,804B, BPFP=1.1602 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,849,172B, BPFP=1.1738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,260,288B, BPFP=0.8000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,264,548B, BPFP=0.8027 +⌛️ [2/4] FRONTEND: Frontend time: 32.732s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.853s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14493624 0.35881610 + layer.9.1 0.14482686 0.36286120 + layer.19.0 0.11946148 21.12302593 + layer.19.1 0.12828579 27.97069487 + layer.29.0 0.10467725 89.70346523 + layer.29.1 0.10613328 151.16408027 + layer.39.0 22.00188902 2751.04647384 + layer.39.1 19.26198661 2709.98033799 + ------------------------------------------------------------------------------------- + TOTAL 5.25152457 718.96371943 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10381544 +BPFP 0.8237 bits/point +EBPFP 0.8237 equivalent bits/point +MSE 718.963719 +---------------------- ---------------------------------------------------------- +Time: 65.588s Load: 1.003s, Pack+Encode: 32.732s, Decode+Unpack: 31.853s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 718.9637 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.123s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 731,984B, BPFP=0.4646 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 728,940B, BPFP=0.4627 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,400,748B, BPFP=0.8891 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,380,056B, BPFP=0.8760 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,821,168B, BPFP=1.1560 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,793,956B, BPFP=1.1387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,292,212B, BPFP=0.8202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,280,444B, BPFP=0.8128 +⌛️ [2/4] FRONTEND: Frontend time: 33.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14475438 4.25160719 + layer.9.1 0.14492096 0.35953329 + layer.19.0 0.11744098 45.73323753 + layer.19.1 0.11578254 41.33487112 + layer.29.0 0.11402616 224.22127884 + layer.29.1 0.11062706 165.11402137 + layer.39.0 28.92800668 3246.69483263 + layer.39.1 10.80449708 3074.20084498 + ------------------------------------------------------------------------------------- + TOTAL 5.06000698 850.23877837 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10429508 +BPFP 0.8275 bits/point +EBPFP 0.8275 equivalent bits/point +MSE 850.238778 +---------------------- ---------------------------------------------------------- +Time: 66.156s Load: 1.123s, Pack+Encode: 33.505s, Decode+Unpack: 31.528s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 850.2388 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.963s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 608,148B, BPFP=0.3860 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 612,420B, BPFP=0.3887 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,166,136B, BPFP=0.7402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,167,924B, BPFP=0.7413 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,440,384B, BPFP=0.9143 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,434,492B, BPFP=0.9105 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 959,176B, BPFP=0.6088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 945,656B, BPFP=0.6003 +⌛️ [2/4] FRONTEND: Frontend time: 33.118s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.815s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14546214 0.33881934 + layer.9.1 0.14553630 4.30860073 + layer.19.0 0.04765745 34.44113077 + layer.19.1 0.04191649 35.71754651 + layer.29.0 0.16505912 95.87880850 + layer.29.1 0.15755973 144.99393687 + layer.39.0 42.51041751 1763.94068898 + layer.39.1 31.38856333 1609.32889178 + ------------------------------------------------------------------------------------- + TOTAL 9.32527151 461.11855294 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8334336 +BPFP 0.6613 bits/point +EBPFP 0.6613 equivalent bits/point +MSE 461.118553 +---------------------- ---------------------------------------------------------- +Time: 65.897s Load: 0.963s, Pack+Encode: 33.118s, Decode+Unpack: 31.815s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.1186 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.930s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,608B, BPFP=0.4161 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 663,016B, BPFP=0.4208 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,305,372B, BPFP=0.8286 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,308,228B, BPFP=0.8304 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,633,408B, BPFP=1.0368 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,622,368B, BPFP=1.0298 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,148,576B, BPFP=0.7291 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,173,392B, BPFP=0.7448 +⌛️ [2/4] FRONTEND: Frontend time: 32.918s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03343166 0.34576422 + layer.9.1 0.03311388 3.01040990 + layer.19.0 0.03842411 9.84604462 + layer.19.1 0.03806642 24.78485792 + layer.29.0 4.26870163 36.53005667 + layer.29.1 4.26552788 53.28349955 + layer.39.0 33.95300821 2316.01771206 + layer.39.1 48.19954501 2397.77201820 + ------------------------------------------------------------------------------------- + TOTAL 11.35372735 605.19879539 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9509968 +BPFP 0.7546 bits/point +EBPFP 0.7546 equivalent bits/point +MSE 605.198795 +---------------------- ---------------------------------------------------------- +Time: 65.455s Load: 0.930s, Pack+Encode: 32.918s, Decode+Unpack: 31.607s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 605.1988 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.929s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 683,988B, BPFP=0.4342 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 680,120B, BPFP=0.4317 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,295,120B, BPFP=0.8221 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,279,952B, BPFP=0.8124 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,583,940B, BPFP=1.0054 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,567,176B, BPFP=0.9948 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,114,724B, BPFP=0.7076 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,114,524B, BPFP=0.7074 +⌛️ [2/4] FRONTEND: Frontend time: 33.471s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549638 0.35387013 + layer.9.1 0.14520178 0.36753261 + layer.19.0 0.11487435 23.65099427 + layer.19.1 0.11481158 9.76852644 + layer.29.0 0.10827909 40.37901670 + layer.29.1 0.10618535 40.77894459 + layer.39.0 9.83978281 2344.58401040 + layer.39.1 9.67554703 2606.66607085 + ------------------------------------------------------------------------------------- + TOTAL 2.53127230 633.31862075 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9319544 +BPFP 0.7394 bits/point +EBPFP 0.7394 equivalent bits/point +MSE 633.318621 +---------------------- ---------------------------------------------------------- +Time: 66.428s Load: 0.929s, Pack+Encode: 33.471s, Decode+Unpack: 32.027s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 633.3186 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.989s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 651,312B, BPFP=0.4134 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 647,344B, BPFP=0.4109 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,245,588B, BPFP=0.7906 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,248,896B, BPFP=0.7927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,614,728B, BPFP=1.0249 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,634,316B, BPFP=1.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,290,664B, BPFP=0.8192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,246,040B, BPFP=0.7909 +⌛️ [2/4] FRONTEND: Frontend time: 32.246s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092318 4.38049979 + layer.9.1 0.00095285 0.33944513 + layer.19.0 0.08568402 8.89697694 + layer.19.1 0.08404610 22.89940486 + layer.29.0 0.12100375 42.15644042 + layer.29.1 0.12795564 41.70425993 + layer.39.0 12.85620633 2926.43126422 + layer.39.1 12.98640239 3010.21221969 + ------------------------------------------------------------------------------------- + TOTAL 3.28289678 757.12756387 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9578888 +BPFP 0.7600 bits/point +EBPFP 0.7600 equivalent bits/point +MSE 757.127564 +---------------------- ---------------------------------------------------------- +Time: 64.737s Load: 0.989s, Pack+Encode: 32.246s, Decode+Unpack: 31.502s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 757.1276 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.112s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,900B, BPFP=0.4208 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 660,204B, BPFP=0.4191 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,255,364B, BPFP=0.7968 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,260,792B, BPFP=0.8003 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,612,544B, BPFP=1.0236 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,616,296B, BPFP=1.0259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,024,416B, BPFP=0.6502 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,044,948B, BPFP=0.6633 +⌛️ [2/4] FRONTEND: Frontend time: 33.231s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.281s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00113376 3.04683025 + layer.9.1 0.00100095 0.33660184 + layer.19.0 0.00983371 7.55528924 + layer.19.1 0.00806405 6.12632980 + layer.29.0 4.28365570 54.80764036 + layer.29.1 4.28597952 49.95526284 + layer.39.0 8.41906814 1638.18947026 + layer.39.1 8.59662605 1848.65453364 + ------------------------------------------------------------------------------------- + TOTAL 3.20067024 451.08399478 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9137464 +BPFP 0.7250 bits/point +EBPFP 0.7250 equivalent bits/point +MSE 451.083995 +---------------------- ---------------------------------------------------------- +Time: 65.624s Load: 1.112s, Pack+Encode: 33.231s, Decode+Unpack: 31.281s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 451.0840 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.933s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 739,516B, BPFP=0.4694 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 740,044B, BPFP=0.4697 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,327,956B, BPFP=0.8429 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,326,056B, BPFP=0.8417 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,715,660B, BPFP=1.0890 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,693,972B, BPFP=1.0752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,060,828B, BPFP=0.6734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,050,968B, BPFP=0.6671 +⌛️ [2/4] FRONTEND: Frontend time: 33.314s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.624s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550294 0.35140167 + layer.9.1 0.14526658 4.23729453 + layer.19.0 0.11599200 11.15560509 + layer.19.1 0.11361485 3.13308735 + layer.29.0 4.26439454 32.63175120 + layer.29.1 4.25587461 31.14488798 + layer.39.0 8.37236706 1725.52356191 + layer.39.1 8.35116642 1736.10562236 + ------------------------------------------------------------------------------------- + TOTAL 3.22052237 443.03540151 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9655000 +BPFP 0.7661 bits/point +EBPFP 0.7661 equivalent bits/point +MSE 443.035402 +---------------------- ---------------------------------------------------------- +Time: 65.871s Load: 0.933s, Pack+Encode: 33.314s, Decode+Unpack: 31.624s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 443.0354 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.987s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,712B, BPFP=0.4295 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 679,908B, BPFP=0.4316 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,362,800B, BPFP=0.8650 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,368,436B, BPFP=0.8686 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,742,920B, BPFP=1.1063 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,724,072B, BPFP=1.0944 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,185,888B, BPFP=0.7527 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,174,296B, BPFP=0.7454 +⌛️ [2/4] FRONTEND: Frontend time: 33.349s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00080404 0.34858179 + layer.9.1 0.00082438 3.04214865 + layer.19.0 0.00843097 22.08524689 + layer.19.1 0.00674472 7.71450225 + layer.29.0 4.27713270 42.36161440 + layer.29.1 4.27133426 31.43087321 + layer.39.0 22.97048921 2627.73431914 + layer.39.1 18.06488920 2713.03233669 + ------------------------------------------------------------------------------------- + TOTAL 6.20008118 680.96870288 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9915032 +BPFP 0.7867 bits/point +EBPFP 0.7867 equivalent bits/point +MSE 680.968703 +---------------------- ---------------------------------------------------------- +Time: 65.866s Load: 0.987s, Pack+Encode: 33.349s, Decode+Unpack: 31.530s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 680.9687 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.970s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 663,228B, BPFP=0.4210 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 668,008B, BPFP=0.4240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,359,556B, BPFP=0.8630 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,367,836B, BPFP=0.8682 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,772,092B, BPFP=1.1248 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,794,564B, BPFP=1.1391 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,229,684B, BPFP=0.7805 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,203,328B, BPFP=0.7638 +⌛️ [2/4] FRONTEND: Frontend time: 33.357s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14506060 4.39849717 + layer.9.1 0.14523201 4.23987320 + layer.19.0 0.04621643 40.18402411 + layer.19.1 0.04629335 27.34336407 + layer.29.0 4.27940669 41.25307473 + layer.29.1 4.27759670 42.29498852 + layer.39.0 19.91382637 2758.28274293 + layer.39.1 24.01088215 2397.24130647 + ------------------------------------------------------------------------------------- + TOTAL 6.60806429 664.40473390 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10058296 +BPFP 0.7981 bits/point +EBPFP 0.7981 equivalent bits/point +MSE 664.404734 +---------------------- ---------------------------------------------------------- +Time: 66.317s Load: 0.970s, Pack+Encode: 33.357s, Decode+Unpack: 31.991s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 664.4047 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.939s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 565,768B, BPFP=0.3591 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 562,788B, BPFP=0.3572 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,032,548B, BPFP=0.6554 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,032,876B, BPFP=0.6556 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,257,684B, BPFP=0.7983 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,259,052B, BPFP=0.7992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 797,852B, BPFP=0.5064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 813,804B, BPFP=0.5166 +⌛️ [2/4] FRONTEND: Frontend time: 32.662s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.582s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66985745 0.32776980 + layer.9.1 2.66884121 0.32990045 + layer.19.0 3.21935619 5.43620003 + layer.19.1 3.21606501 7.48488280 + layer.29.0 4.24164606 37.05492109 + layer.29.1 4.23648681 36.19819934 + layer.39.0 8.06392628 1158.77591810 + layer.39.1 8.17747540 1272.79647384 + ------------------------------------------------------------------------------------- + TOTAL 4.56170680 314.80053318 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7322372 +BPFP 0.5810 bits/point +EBPFP 0.5810 equivalent bits/point +MSE 314.800533 +---------------------- ---------------------------------------------------------- +Time: 65.183s Load: 0.939s, Pack+Encode: 32.662s, Decode+Unpack: 31.582s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 314.8005 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.866s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 569,344B, BPFP=0.3614 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 569,468B, BPFP=0.3615 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,081,084B, BPFP=0.6862 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,078,984B, BPFP=0.6849 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,397,156B, BPFP=0.8868 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,382,004B, BPFP=0.8772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 944,888B, BPFP=0.5998 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 943,208B, BPFP=0.5987 +⌛️ [2/4] FRONTEND: Frontend time: 33.086s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.280s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66795352 3.05962776 + layer.9.1 2.66862889 4.39630696 + layer.19.0 3.22250645 21.61005545 + layer.19.1 3.22577319 12.68885836 + layer.29.0 4.25792136 27.18771835 + layer.29.1 4.25014663 31.67766392 + layer.39.0 8.65209937 1678.07491063 + layer.39.1 8.58450170 1553.13779656 + ------------------------------------------------------------------------------------- + TOTAL 4.69119139 416.47911725 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7966136 +BPFP 0.6321 bits/point +EBPFP 0.6321 equivalent bits/point +MSE 416.479117 +---------------------- ---------------------------------------------------------- +Time: 65.232s Load: 0.866s, Pack+Encode: 33.086s, Decode+Unpack: 31.280s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 416.4791 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.869s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 681,560B, BPFP=0.4326 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 678,572B, BPFP=0.4307 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,359,376B, BPFP=0.8629 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,357,716B, BPFP=0.8618 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,763,316B, BPFP=1.1193 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,767,000B, BPFP=1.1216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,023,788B, BPFP=0.6498 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,010,672B, BPFP=0.6415 +⌛️ [2/4] FRONTEND: Frontend time: 33.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.788s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092775 4.31001464 + layer.9.1 0.00093166 4.36140747 + layer.19.0 0.08227225 41.41774405 + layer.19.1 0.08381199 28.23609390 + layer.29.0 0.10725604 36.22443228 + layer.29.1 0.10756977 40.14561667 + layer.39.0 7.96294394 1662.81215470 + layer.39.1 7.95922050 1610.39470263 + ------------------------------------------------------------------------------------- + TOTAL 2.03811674 428.48777079 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9642000 +BPFP 0.7650 bits/point +EBPFP 0.7650 equivalent bits/point +MSE 428.487771 +---------------------- ---------------------------------------------------------- +Time: 65.949s Load: 0.869s, Pack+Encode: 33.293s, Decode+Unpack: 31.788s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 428.4878 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.925s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 587,068B, BPFP=0.3726 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 585,808B, BPFP=0.3718 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,144,540B, BPFP=0.7265 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,139,164B, BPFP=0.7231 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,407,504B, BPFP=0.8934 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,399,704B, BPFP=0.8885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 983,212B, BPFP=0.6241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 934,536B, BPFP=0.5932 +⌛️ [2/4] FRONTEND: Frontend time: 32.873s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.690s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66308118 3.11046391 + layer.9.1 2.66351027 4.44019610 + layer.19.0 3.21594155 10.77557788 + layer.19.1 3.21498593 5.24537870 + layer.29.0 4.33566519 60.28684088 + layer.29.1 4.34101296 93.97224366 + layer.39.0 8.65310735 1851.14299643 + layer.39.1 8.66575030 1781.82677933 + ------------------------------------------------------------------------------------- + TOTAL 4.71913184 476.35005961 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8181536 +BPFP 0.6492 bits/point +EBPFP 0.6492 equivalent bits/point +MSE 476.350060 +---------------------- ---------------------------------------------------------- +Time: 65.489s Load: 0.925s, Pack+Encode: 32.873s, Decode+Unpack: 31.690s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 476.3501 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.878s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 594,092B, BPFP=0.3771 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 593,088B, BPFP=0.3765 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,199,964B, BPFP=0.7617 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,197,116B, BPFP=0.7599 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,510,480B, BPFP=0.9588 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,520,000B, BPFP=0.9648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,032,592B, BPFP=0.6554 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,030,144B, BPFP=0.6539 +⌛️ [2/4] FRONTEND: Frontend time: 32.860s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.809s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.65959259 4.33484129 + layer.9.1 2.65993726 0.33186397 + layer.19.0 3.20866700 9.42855701 + layer.19.1 3.21007805 7.84379697 + layer.29.0 4.27255361 73.67322067 + layer.29.1 4.27602442 40.06053989 + layer.39.0 19.11658068 2896.99090023 + layer.39.1 9.60360322 2869.72375691 + ------------------------------------------------------------------------------------- + TOTAL 6.12587960 737.79843462 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8677476 +BPFP 0.6885 bits/point +EBPFP 0.6885 equivalent bits/point +MSE 737.798435 +---------------------- ---------------------------------------------------------- +Time: 65.548s Load: 0.878s, Pack+Encode: 32.860s, Decode+Unpack: 31.809s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 737.7984 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.873s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 616,644B, BPFP=0.3914 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 609,472B, BPFP=0.3869 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,217,816B, BPFP=0.7730 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,221,912B, BPFP=0.7756 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,473,804B, BPFP=0.9355 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,474,376B, BPFP=0.9359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 976,480B, BPFP=0.6198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 993,588B, BPFP=0.6307 +⌛️ [2/4] FRONTEND: Frontend time: 32.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.847s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67078767 0.34707930 + layer.9.1 2.67131261 0.34020155 + layer.19.0 3.30595795 10.34916695 + layer.19.1 3.30543206 24.93127184 + layer.29.0 0.11228124 60.26264929 + layer.29.1 0.11507649 64.46381926 + layer.39.0 11.41791162 1782.92687683 + layer.39.1 11.38150745 1970.51039974 + ------------------------------------------------------------------------------------- + TOTAL 4.37253339 489.26643310 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8584092 +BPFP 0.6811 bits/point +EBPFP 0.6811 equivalent bits/point +MSE 489.266433 +---------------------- ---------------------------------------------------------- +Time: 65.366s Load: 0.873s, Pack+Encode: 32.645s, Decode+Unpack: 31.847s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 489.2664 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.927s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 665,472B, BPFP=0.4224 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 664,440B, BPFP=0.4218 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,264,696B, BPFP=0.8028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,254,248B, BPFP=0.7961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,665,856B, BPFP=1.0574 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,622,072B, BPFP=1.0296 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,196,172B, BPFP=0.7593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,119,916B, BPFP=0.7109 +⌛️ [2/4] FRONTEND: Frontend time: 31.965s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14463971 0.33978531 + layer.9.1 0.14470460 0.34173693 + layer.19.0 0.12255537 13.06327059 + layer.19.1 0.11825690 22.85405834 + layer.29.0 0.11949990 44.21765315 + layer.29.1 0.11467140 59.72781423 + layer.39.0 10.68243977 3273.93955151 + layer.39.1 10.40156301 2485.32157946 + ------------------------------------------------------------------------------------- + TOTAL 2.73104133 737.47568119 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9452872 +BPFP 0.7500 bits/point +EBPFP 0.7500 equivalent bits/point +MSE 737.475681 +---------------------- ---------------------------------------------------------- +Time: 64.498s Load: 0.927s, Pack+Encode: 31.965s, Decode+Unpack: 31.607s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 737.4757 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.074s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 703,944B, BPFP=0.4468 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 703,496B, BPFP=0.4465 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,361,512B, BPFP=0.8642 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,343,480B, BPFP=0.8528 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,709,356B, BPFP=1.0850 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,668,940B, BPFP=1.0594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,118,972B, BPFP=0.7103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,114,200B, BPFP=0.7072 +⌛️ [2/4] FRONTEND: Frontend time: 32.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.893s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14485273 2.99405335 + layer.9.1 0.14484227 8.36943832 + layer.19.0 0.11969613 48.88658291 + layer.19.1 0.11916645 59.27240921 + layer.29.0 0.11480527 81.40893118 + layer.29.1 0.11451660 62.14755139 + layer.39.0 11.00270276 2359.70539487 + layer.39.1 11.01557422 2326.47400065 + ------------------------------------------------------------------------------------- + TOTAL 2.84701956 618.65729523 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9723900 +BPFP 0.7715 bits/point +EBPFP 0.7715 equivalent bits/point +MSE 618.657295 +---------------------- ---------------------------------------------------------- +Time: 65.563s Load: 1.074s, Pack+Encode: 32.596s, Decode+Unpack: 31.893s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 618.6573 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.155s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 652,588B, BPFP=0.4142 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 652,340B, BPFP=0.4141 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,176,092B, BPFP=0.7465 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,172,892B, BPFP=0.7445 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,465,216B, BPFP=0.9300 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,467,276B, BPFP=0.9314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,077,052B, BPFP=0.6837 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,073,196B, BPFP=0.6812 +⌛️ [2/4] FRONTEND: Frontend time: 32.858s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.357s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14481488 8.58909665 + layer.9.1 0.14470567 8.53841442 + layer.19.0 0.03819180 14.03131601 + layer.19.1 0.04002141 44.52409510 + layer.29.0 0.11241068 88.90475707 + layer.29.1 0.11133552 79.93805858 + layer.39.0 31.78807483 2555.10334742 + layer.39.1 43.50691623 2121.38414040 + ------------------------------------------------------------------------------------- + TOTAL 9.48580888 615.12665321 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8736652 +BPFP 0.6932 bits/point +EBPFP 0.6932 equivalent bits/point +MSE 615.126653 +---------------------- ---------------------------------------------------------- +Time: 65.370s Load: 1.155s, Pack+Encode: 32.858s, Decode+Unpack: 31.357s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.1267 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.147s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 659,932B, BPFP=0.4189 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 660,916B, BPFP=0.4195 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,251,496B, BPFP=0.7944 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,253,428B, BPFP=0.7956 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,624,908B, BPFP=1.0314 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,629,940B, BPFP=1.0346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,314,192B, BPFP=0.8342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,300,764B, BPFP=0.8257 +⌛️ [2/4] FRONTEND: Frontend time: 32.871s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.761s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14528272 4.26060794 + layer.9.1 0.14516892 4.20994412 + layer.19.0 0.11319376 18.55713636 + layer.19.1 0.11666145 30.42695554 + layer.29.0 0.21118872 168.78741469 + layer.29.1 0.20646930 46.76565547 + layer.39.0 14.37750853 4061.89275268 + layer.39.1 21.76644002 3807.88495288 + ------------------------------------------------------------------------------------- + TOTAL 4.63523918 1017.84817746 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9695576 +BPFP 0.7693 bits/point +EBPFP 0.7693 equivalent bits/point +MSE 1017.848177 +---------------------- ---------------------------------------------------------- +Time: 65.778s Load: 1.147s, Pack+Encode: 32.871s, Decode+Unpack: 31.761s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1017.8482 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 629,044B, BPFP=0.3993 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 625,352B, BPFP=0.3969 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,232,336B, BPFP=0.7822 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,218,832B, BPFP=0.7737 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,579,388B, BPFP=1.0025 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,561,492B, BPFP=0.9912 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,083,280B, BPFP=0.6876 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,024,412B, BPFP=0.6502 +⌛️ [2/4] FRONTEND: Frontend time: 33.192s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.695s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14454765 4.24049050 + layer.9.1 0.14475082 8.24376612 + layer.19.0 0.04087094 19.26629783 + layer.19.1 0.11687931 33.72843120 + layer.29.0 0.10817139 38.65650644 + layer.29.1 0.10802081 33.82986168 + layer.39.0 19.80422286 2104.14169646 + layer.39.1 34.29222355 2005.57458564 + ------------------------------------------------------------------------------------- + TOTAL 6.84496092 530.96020448 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8954136 +BPFP 0.7105 bits/point +EBPFP 0.7105 equivalent bits/point +MSE 530.960204 +---------------------- ---------------------------------------------------------- +Time: 65.967s Load: 1.080s, Pack+Encode: 33.192s, Decode+Unpack: 31.695s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 530.9602 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.118s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 587,636B, BPFP=0.3730 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 589,600B, BPFP=0.3742 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,162,392B, BPFP=0.7378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,163,420B, BPFP=0.7385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,395,176B, BPFP=0.8856 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,415,196B, BPFP=0.8983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 945,716B, BPFP=0.6003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 935,244B, BPFP=0.5936 +⌛️ [2/4] FRONTEND: Frontend time: 33.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.879s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14462205 4.30301271 + layer.9.1 0.14495783 4.25291382 + layer.19.0 0.04322015 4.39873583 + layer.19.1 0.03788725 29.51636893 + layer.29.0 0.10021623 39.19272272 + layer.29.1 0.10137775 33.79390945 + layer.39.0 58.66958482 1990.21303217 + layer.39.1 72.48303949 1859.21254469 + ------------------------------------------------------------------------------------- + TOTAL 16.46561320 495.61040504 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8194380 +BPFP 0.6502 bits/point +EBPFP 0.6502 equivalent bits/point +MSE 495.610405 +---------------------- ---------------------------------------------------------- +Time: 66.628s Load: 1.118s, Pack+Encode: 33.631s, Decode+Unpack: 31.879s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 495.6104 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.101s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 705,796B, BPFP=0.4480 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 709,424B, BPFP=0.4503 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,425,296B, BPFP=0.9047 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,434,604B, BPFP=0.9106 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,817,672B, BPFP=1.1538 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,815,656B, BPFP=1.1525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,268,212B, BPFP=0.8050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,267,616B, BPFP=0.8046 +⌛️ [2/4] FRONTEND: Frontend time: 32.967s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.887s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527927 4.22113380 + layer.9.1 0.14528875 0.35173825 + layer.19.0 0.12591341 65.56996466 + layer.19.1 0.13556211 11.66696204 + layer.29.0 0.11238900 69.06218516 + layer.29.1 0.11028371 42.47330496 + layer.39.0 11.48751193 3480.18752031 + layer.39.1 11.29491489 3583.16509587 + ------------------------------------------------------------------------------------- + TOTAL 2.94464289 907.08723813 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10444276 +BPFP 0.8287 bits/point +EBPFP 0.8287 equivalent bits/point +MSE 907.087238 +---------------------- ---------------------------------------------------------- +Time: 65.956s Load: 1.101s, Pack+Encode: 32.967s, Decode+Unpack: 31.887s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 907.0872 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.045s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 688,380B, BPFP=0.4369 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 689,564B, BPFP=0.4377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,325,000B, BPFP=0.8410 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,329,156B, BPFP=0.8437 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,639,992B, BPFP=1.0410 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,674,608B, BPFP=1.0630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,078,968B, BPFP=0.6849 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,070,912B, BPFP=0.6798 +⌛️ [2/4] FRONTEND: Frontend time: 33.467s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.572s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14489184 41.55295082 + layer.9.1 0.14511764 12.63458345 + layer.19.0 0.03976490 14.78235066 + layer.19.1 0.11370806 17.84575962 + layer.29.0 0.10933599 39.37541640 + layer.29.1 0.11012027 79.86491510 + layer.39.0 9.10787636 2160.56028599 + layer.39.1 9.00026152 2230.08823529 + ------------------------------------------------------------------------------------- + TOTAL 2.34638457 574.58806217 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9496580 +BPFP 0.7535 bits/point +EBPFP 0.7535 equivalent bits/point +MSE 574.588062 +---------------------- ---------------------------------------------------------- +Time: 66.084s Load: 1.045s, Pack+Encode: 33.467s, Decode+Unpack: 31.572s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 574.5881 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.970s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,408B, BPFP=0.3925 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 617,428B, BPFP=0.3919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,261,756B, BPFP=0.8009 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,238,932B, BPFP=0.7864 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,474,164B, BPFP=0.9357 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,482,396B, BPFP=0.9410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 856,092B, BPFP=0.5434 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 846,196B, BPFP=0.5371 +⌛️ [2/4] FRONTEND: Frontend time: 33.729s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083307 8.63297880 + layer.9.1 0.00247171 4.30002844 + layer.19.0 0.00642632 18.95620227 + layer.19.1 0.00641681 19.51901329 + layer.29.0 0.10256791 34.73438008 + layer.29.1 0.10162673 36.01293366 + layer.39.0 8.50517638 1757.18752031 + layer.39.1 8.55767781 1795.93955151 + ------------------------------------------------------------------------------------- + TOTAL 2.16039959 459.41032605 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8395372 +BPFP 0.6661 bits/point +EBPFP 0.6661 equivalent bits/point +MSE 459.410326 +---------------------- ---------------------------------------------------------- +Time: 66.169s Load: 0.970s, Pack+Encode: 33.729s, Decode+Unpack: 31.471s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 459.4103 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.993s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 630,956B, BPFP=0.4005 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 631,912B, BPFP=0.4011 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,218,700B, BPFP=0.7736 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,224,916B, BPFP=0.7775 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,640,140B, BPFP=1.0411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,636,792B, BPFP=1.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,133,232B, BPFP=0.7193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,145,424B, BPFP=0.7271 +⌛️ [2/4] FRONTEND: Frontend time: 32.824s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.635s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00064391 4.27036658 + layer.9.1 0.00065402 0.33294037 + layer.19.0 0.08134466 7.84284739 + layer.19.1 0.08141702 7.04987229 + layer.29.0 0.11551180 46.25826698 + layer.29.1 0.11251285 46.90174582 + layer.39.0 10.61319619 2900.38966526 + layer.39.1 10.43102047 3004.40298993 + ------------------------------------------------------------------------------------- + TOTAL 2.67953762 752.18108683 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9262072 +BPFP 0.7349 bits/point +EBPFP 0.7349 equivalent bits/point +MSE 752.181087 +---------------------- ---------------------------------------------------------- +Time: 65.452s Load: 0.993s, Pack+Encode: 32.824s, Decode+Unpack: 31.635s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 752.1811 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.021s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 653,532B, BPFP=0.4148 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 655,860B, BPFP=0.4163 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,271,412B, BPFP=0.8070 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,267,524B, BPFP=0.8046 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,627,256B, BPFP=1.0329 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,628,484B, BPFP=1.0337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,301,228B, BPFP=0.8260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,312,716B, BPFP=0.8332 +⌛️ [2/4] FRONTEND: Frontend time: 33.100s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.413s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14441776 29.21634811 + layer.9.1 0.14449203 4.96084387 + layer.19.0 0.11315974 32.07295052 + layer.19.1 0.11435745 17.84194096 + layer.29.0 0.12811458 64.88799460 + layer.29.1 0.12952277 45.37957528 + layer.39.0 31.10682331 3157.78648034 + layer.39.1 16.99297713 3751.51511212 + ------------------------------------------------------------------------------------- + TOTAL 6.10923309 887.95765572 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9718012 +BPFP 0.7711 bits/point +EBPFP 0.7711 equivalent bits/point +MSE 887.957656 +---------------------- ---------------------------------------------------------- +Time: 65.535s Load: 1.021s, Pack+Encode: 33.100s, Decode+Unpack: 31.413s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 887.9577 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.030s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,664B, BPFP=0.4060 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 635,652B, BPFP=0.4035 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,263,428B, BPFP=0.8020 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,259,080B, BPFP=0.7992 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,564,616B, BPFP=0.9931 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,554,396B, BPFP=0.9867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,145,696B, BPFP=0.7272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,136,744B, BPFP=0.7215 +⌛️ [2/4] FRONTEND: Frontend time: 32.683s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.953s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078516 0.33449297 + layer.9.1 0.00079184 0.33546013 + layer.19.0 3.22632161 20.46613737 + layer.19.1 3.22513146 11.33869739 + layer.29.0 0.10494786 59.00739357 + layer.29.1 0.10251782 43.89227027 + layer.39.0 10.88842496 3171.30159246 + layer.39.1 10.78217420 2875.80760481 + ------------------------------------------------------------------------------------- + TOTAL 3.54138686 772.81045612 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9199276 +BPFP 0.7299 bits/point +EBPFP 0.7299 equivalent bits/point +MSE 772.810456 +---------------------- ---------------------------------------------------------- +Time: 65.666s Load: 1.030s, Pack+Encode: 32.683s, Decode+Unpack: 31.953s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 772.8105 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 633,468B, BPFP=0.4021 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 628,216B, BPFP=0.3988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,170,256B, BPFP=0.7428 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,189,348B, BPFP=0.7549 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,582,084B, BPFP=1.0042 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,600,652B, BPFP=1.0160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,131,720B, BPFP=0.7184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,179,844B, BPFP=0.7489 +⌛️ [2/4] FRONTEND: Frontend time: 32.783s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.754s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561636 65.97616388 + layer.9.1 0.14552785 12.48251772 + layer.19.0 0.04069186 13.67288298 + layer.19.1 0.03840616 25.05142235 + layer.29.0 0.11346353 43.01994130 + layer.29.1 0.11182956 43.83998720 + layer.39.0 10.19697364 3760.06824829 + layer.39.1 10.11578978 3968.38381540 + ------------------------------------------------------------------------------------- + TOTAL 2.61353734 991.56187239 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9115588 +BPFP 0.7233 bits/point +EBPFP 0.7233 equivalent bits/point +MSE 991.561872 +---------------------- ---------------------------------------------------------- +Time: 65.712s Load: 1.175s, Pack+Encode: 32.783s, Decode+Unpack: 31.754s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 991.5619 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.064s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 643,368B, BPFP=0.4084 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 643,456B, BPFP=0.4084 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,290,128B, BPFP=0.8189 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,284,104B, BPFP=0.8151 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,658,908B, BPFP=1.0530 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,643,432B, BPFP=1.0432 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,132,364B, BPFP=0.7188 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,153,296B, BPFP=0.7321 +⌛️ [2/4] FRONTEND: Frontend time: 32.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.682s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14399445 0.34182548 + layer.9.1 0.14558028 4.35496095 + layer.19.0 0.03837104 12.51558818 + layer.19.1 0.04376782 25.19406077 + layer.29.0 0.11695251 83.37351722 + layer.29.1 0.13128335 50.82961285 + layer.39.0 11.28613757 2249.40948976 + layer.39.1 11.84408769 2307.36269093 + ------------------------------------------------------------------------------------- + TOTAL 2.96877184 591.67271827 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9449056 +BPFP 0.7497 bits/point +EBPFP 0.7497 equivalent bits/point +MSE 591.672718 +---------------------- ---------------------------------------------------------- +Time: 65.369s Load: 1.064s, Pack+Encode: 32.624s, Decode+Unpack: 31.682s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 591.6727 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.110s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,436B, BPFP=0.4059 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 634,040B, BPFP=0.4025 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,337,576B, BPFP=0.8490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,345,724B, BPFP=0.8542 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,878,012B, BPFP=1.1921 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,875,596B, BPFP=1.1905 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,590,084B, BPFP=1.0093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,599,504B, BPFP=1.0153 +⌛️ [2/4] FRONTEND: Frontend time: 33.313s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14502686 0.33343912 + layer.9.1 0.03259508 4.23499261 + layer.19.0 0.11326540 20.62698550 + layer.19.1 0.11324834 17.67336793 + layer.29.0 0.12250664 92.87752884 + layer.29.1 0.12058897 73.26338052 + layer.39.0 16.17915050 4531.27624309 + layer.39.1 21.66230805 5369.16152096 + ------------------------------------------------------------------------------------- + TOTAL 4.81108623 1263.68093232 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10899972 +BPFP 0.8648 bits/point +EBPFP 0.8648 equivalent bits/point +MSE 1263.680932 +---------------------- ---------------------------------------------------------- +Time: 65.954s Load: 1.110s, Pack+Encode: 33.313s, Decode+Unpack: 31.531s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1263.6809 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.957s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 612,784B, BPFP=0.3890 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 616,672B, BPFP=0.3914 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,179,936B, BPFP=0.7490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,182,132B, BPFP=0.7504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,447,932B, BPFP=0.9191 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,461,504B, BPFP=0.9277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 895,584B, BPFP=0.5685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 897,972B, BPFP=0.5700 +⌛️ [2/4] FRONTEND: Frontend time: 33.028s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.767s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66733019 0.59661197 + layer.9.1 2.66763138 4.36256747 + layer.19.0 3.22293078 9.17737701 + layer.19.1 3.22376992 6.36286803 + layer.29.0 4.27658332 24.68230013 + layer.29.1 4.27160529 29.43864001 + layer.39.0 7.81683598 1364.99870003 + layer.39.1 9.86231960 1334.58019175 + ------------------------------------------------------------------------------------- + TOTAL 4.75112581 346.77490705 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8294516 +BPFP 0.6581 bits/point +EBPFP 0.6581 equivalent bits/point +MSE 346.774907 +---------------------- ---------------------------------------------------------- +Time: 65.753s Load: 0.957s, Pack+Encode: 33.028s, Decode+Unpack: 31.767s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 346.7749 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.924s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,156B, BPFP=0.3829 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 602,212B, BPFP=0.3823 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,113,816B, BPFP=0.7070 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,123,848B, BPFP=0.7134 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,376,736B, BPFP=0.8739 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,392,208B, BPFP=0.8837 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 820,792B, BPFP=0.5210 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 839,256B, BPFP=0.5327 +⌛️ [2/4] FRONTEND: Frontend time: 33.330s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549763 4.33209822 + layer.9.1 0.14520254 4.23259421 + layer.19.0 0.04746155 34.80233639 + layer.19.1 0.04383140 25.06202013 + layer.29.0 4.26247378 39.18315069 + layer.29.1 4.25497898 34.80354749 + layer.39.0 7.94138086 1336.86447839 + layer.39.1 7.86439079 1300.57653559 + ------------------------------------------------------------------------------------- + TOTAL 3.08815219 347.48209514 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 7872024 +BPFP 0.6246 bits/point +EBPFP 0.6246 equivalent bits/point +MSE 347.482095 +---------------------- ---------------------------------------------------------- +Time: 65.771s Load: 0.924s, Pack+Encode: 33.330s, Decode+Unpack: 31.517s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 347.4821 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.996s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 579,856B, BPFP=0.3681 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 578,124B, BPFP=0.3670 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,079,036B, BPFP=0.6849 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,076,496B, BPFP=0.6833 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,399,720B, BPFP=0.8885 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,368,084B, BPFP=0.8684 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,144,780B, BPFP=0.7266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,108,584B, BPFP=0.7037 +⌛️ [2/4] FRONTEND: Frontend time: 33.242s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.699s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11283708 8.44622019 + layer.9.1 0.11300174 4.35114039 + layer.19.0 3.22718329 40.39503778 + layer.19.1 3.22892155 21.02066491 + layer.29.0 4.26448309 42.69458889 + layer.29.1 4.25758082 30.71236696 + layer.39.0 9.82393946 2822.72148196 + layer.39.1 9.78394007 2921.01137472 + ------------------------------------------------------------------------------------- + TOTAL 4.35148589 736.41910947 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8334680 +BPFP 0.6613 bits/point +EBPFP 0.6613 equivalent bits/point +MSE 736.419109 +---------------------- ---------------------------------------------------------- +Time: 65.937s Load: 0.996s, Pack+Encode: 33.242s, Decode+Unpack: 31.699s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 736.4191 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.931s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 760,840B, BPFP=0.4829 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 760,340B, BPFP=0.4826 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,345,040B, BPFP=0.8538 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,347,980B, BPFP=0.8556 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,684,496B, BPFP=1.0692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,675,328B, BPFP=1.0634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,250,380B, BPFP=0.7937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,240,956B, BPFP=0.7877 +⌛️ [2/4] FRONTEND: Frontend time: 32.688s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.936s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14503861 0.37596462 + layer.9.1 0.14483112 0.76420546 + layer.19.0 0.11529889 26.76097863 + layer.19.1 0.11517203 46.45208401 + layer.29.0 0.11961639 52.21729261 + layer.29.1 0.11795276 45.88362752 + layer.39.0 83.84633978 2874.56288593 + layer.39.1 174.87768118 3088.20831979 + ------------------------------------------------------------------------------------- + TOTAL 32.43524135 766.90316982 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10065360 +BPFP 0.7986 bits/point +EBPFP 0.7986 equivalent bits/point +MSE 766.903170 +---------------------- ---------------------------------------------------------- +Time: 65.555s Load: 0.931s, Pack+Encode: 32.688s, Decode+Unpack: 31.936s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 766.9032 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.120s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 598,824B, BPFP=0.3801 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 599,432B, BPFP=0.3805 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,147,160B, BPFP=0.7282 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,153,352B, BPFP=0.7321 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,487,016B, BPFP=0.9439 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,496,796B, BPFP=0.9501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,035,016B, BPFP=0.6570 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,050,008B, BPFP=0.6665 +⌛️ [2/4] FRONTEND: Frontend time: 32.979s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.81453913 4.23283510 + layer.9.1 0.14528001 3.01741531 + layer.19.0 3.26598681 8.39933694 + layer.19.1 0.04116655 8.42385478 + layer.29.0 4.28557138 50.83350260 + layer.29.1 4.28198282 64.54680899 + layer.39.0 74.89367180 1932.04176146 + layer.39.1 42.04871577 2220.79054274 + ------------------------------------------------------------------------------------- + TOTAL 16.47211428 536.53575724 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8567604 +BPFP 0.6798 bits/point +EBPFP 0.6798 equivalent bits/point +MSE 536.535757 +---------------------- ---------------------------------------------------------- +Time: 65.604s Load: 1.120s, Pack+Encode: 32.979s, Decode+Unpack: 31.505s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 536.5358 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.970s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 608,568B, BPFP=0.3863 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 614,152B, BPFP=0.3898 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,217,624B, BPFP=0.7729 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,214,604B, BPFP=0.7710 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,527,504B, BPFP=0.9696 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,525,996B, BPFP=0.9686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 929,700B, BPFP=0.5901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 964,008B, BPFP=0.6119 +⌛️ [2/4] FRONTEND: Frontend time: 33.122s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66710422 8.38842432 + layer.9.1 2.66812426 4.36709165 + layer.19.0 3.22059776 10.45736132 + layer.19.1 3.22546153 12.61436921 + layer.29.0 0.11226317 56.26282195 + layer.29.1 0.11257672 46.08230419 + layer.39.0 59.39237691 2014.23236919 + layer.39.1 37.52358222 1983.54728632 + ------------------------------------------------------------------------------------- + TOTAL 13.61526085 516.99400352 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8602156 +BPFP 0.6825 bits/point +EBPFP 0.6825 equivalent bits/point +MSE 516.994004 +---------------------- ---------------------------------------------------------- +Time: 65.574s Load: 0.970s, Pack+Encode: 33.122s, Decode+Unpack: 31.482s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 516.9940 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.875s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 601,684B, BPFP=0.3819 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 600,252B, BPFP=0.3810 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,219,216B, BPFP=0.7739 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,210,836B, BPFP=0.7686 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,513,636B, BPFP=0.9608 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,521,084B, BPFP=0.9655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 910,764B, BPFP=0.5781 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 923,756B, BPFP=0.5864 +⌛️ [2/4] FRONTEND: Frontend time: 33.358s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.214s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519123 23.38390427 + layer.9.1 0.14511500 4.25946729 + layer.19.0 0.03974548 9.53632355 + layer.19.1 0.03981401 8.58697341 + layer.29.0 4.26343511 89.90953039 + layer.29.1 4.25610090 45.32489032 + layer.39.0 7.90972018 1802.94296393 + layer.39.1 8.05601540 1685.88462788 + ------------------------------------------------------------------------------------- + TOTAL 3.10689216 458.72858513 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8501228 +BPFP 0.6745 bits/point +EBPFP 0.6745 equivalent bits/point +MSE 458.728585 +---------------------- ---------------------------------------------------------- +Time: 65.447s Load: 0.875s, Pack+Encode: 33.358s, Decode+Unpack: 31.214s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 458.7286 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.817s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 604,020B, BPFP=0.3834 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 604,032B, BPFP=0.3834 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,198,628B, BPFP=0.7608 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,195,032B, BPFP=0.7585 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,644,376B, BPFP=1.0438 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,653,100B, BPFP=1.0493 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,044,956B, BPFP=0.6633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,073,476B, BPFP=0.6814 +⌛️ [2/4] FRONTEND: Frontend time: 33.476s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.736s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14586543 4.30972202 + layer.9.1 0.14572574 4.25610375 + layer.19.0 0.03953905 8.28548378 + layer.19.1 0.03760033 7.97411237 + layer.29.0 0.10448607 82.83177100 + layer.29.1 0.10697372 116.56487650 + layer.39.0 14.19073468 2431.29899253 + layer.39.1 8.92149669 2287.79168021 + ------------------------------------------------------------------------------------- + TOTAL 2.96155271 617.91409277 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9017620 +BPFP 0.7155 bits/point +EBPFP 0.7155 equivalent bits/point +MSE 617.914093 +---------------------- ---------------------------------------------------------- +Time: 66.029s Load: 0.817s, Pack+Encode: 33.476s, Decode+Unpack: 31.736s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 617.9141 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.820s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 643,864B, BPFP=0.4087 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 645,380B, BPFP=0.4097 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,315,008B, BPFP=0.8347 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,306,700B, BPFP=0.8294 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,640,028B, BPFP=1.0410 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,645,920B, BPFP=1.0447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,050,124B, BPFP=0.6666 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,060,192B, BPFP=0.6730 +⌛️ [2/4] FRONTEND: Frontend time: 33.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.918s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00251883 3.02375773 + layer.9.1 0.14409062 0.34160550 + layer.19.0 0.12740102 92.88365291 + layer.19.1 0.12254588 42.48573590 + layer.29.0 4.25147928 35.28661998 + layer.29.1 4.25065697 56.35508409 + layer.39.0 9.21805114 2368.35424114 + layer.39.1 9.03214690 2161.46262593 + ------------------------------------------------------------------------------------- + TOTAL 3.39361133 595.02416540 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9307216 +BPFP 0.7385 bits/point +EBPFP 0.7385 equivalent bits/point +MSE 595.024165 +---------------------- ---------------------------------------------------------- +Time: 66.024s Load: 0.820s, Pack+Encode: 33.287s, Decode+Unpack: 31.918s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 595.0242 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.820s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 729,532B, BPFP=0.4631 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 726,620B, BPFP=0.4612 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,338,600B, BPFP=0.8497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,337,684B, BPFP=0.8491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,752,532B, BPFP=1.1124 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,750,280B, BPFP=1.1110 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,181,732B, BPFP=0.7501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,173,608B, BPFP=0.7449 +⌛️ [2/4] FRONTEND: Frontend time: 32.970s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.849s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550905 4.58432047 + layer.9.1 0.14590163 4.28889112 + layer.19.0 0.12839093 43.02813211 + layer.19.1 0.12422524 40.99160353 + layer.29.0 0.11695262 47.13238341 + layer.29.1 0.11389293 51.71044240 + layer.39.0 10.18180439 2627.29216770 + layer.39.1 10.42432323 2469.18882028 + ------------------------------------------------------------------------------------- + TOTAL 2.67262500 661.02709513 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9990588 +BPFP 0.7927 bits/point +EBPFP 0.7927 equivalent bits/point +MSE 661.027095 +---------------------- ---------------------------------------------------------- +Time: 65.639s Load: 0.820s, Pack+Encode: 32.970s, Decode+Unpack: 31.849s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 661.0271 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.815s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 653,688B, BPFP=0.4149 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 655,520B, BPFP=0.4161 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,281,680B, BPFP=0.8135 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,297,636B, BPFP=0.8237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,698,784B, BPFP=1.0783 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,711,800B, BPFP=1.0866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,158,380B, BPFP=0.7353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,193,880B, BPFP=0.7578 +⌛️ [2/4] FRONTEND: Frontend time: 32.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.789s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509775 0.35107097 + layer.9.1 0.14508723 0.34535913 + layer.19.0 0.11633494 12.72122806 + layer.19.1 0.11804005 17.75820731 + layer.29.0 0.15409572 101.45588235 + layer.29.1 0.14997486 64.96544422 + layer.39.0 9.23291952 3164.32629184 + layer.39.1 9.22304726 2933.51738707 + ------------------------------------------------------------------------------------- + TOTAL 2.41057467 786.93010887 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9651368 +BPFP 0.7658 bits/point +EBPFP 0.7658 equivalent bits/point +MSE 786.930109 +---------------------- ---------------------------------------------------------- +Time: 64.757s Load: 0.815s, Pack+Encode: 32.153s, Decode+Unpack: 31.789s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 786.9301 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.914s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,840B, BPFP=0.4417 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 697,424B, BPFP=0.4427 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,314,804B, BPFP=0.8346 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,322,764B, BPFP=0.8396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,713,548B, BPFP=1.0877 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,713,240B, BPFP=1.0875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,208,360B, BPFP=0.7670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,260,000B, BPFP=0.7998 +⌛️ [2/4] FRONTEND: Frontend time: 33.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.966s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497060 4.60838638 + layer.9.1 0.14492971 4.27078266 + layer.19.0 0.11929473 70.12981902 + layer.19.1 0.11869117 37.06961428 + layer.29.0 0.13715227 55.14321986 + layer.29.1 0.14278979 99.64125975 + layer.39.0 9.99110525 2908.15989600 + layer.39.1 10.01170034 3682.66720832 + ------------------------------------------------------------------------------------- + TOTAL 2.60132923 857.71127328 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9925980 +BPFP 0.7876 bits/point +EBPFP 0.7876 equivalent bits/point +MSE 857.711273 +---------------------- ---------------------------------------------------------- +Time: 66.536s Load: 0.914s, Pack+Encode: 33.656s, Decode+Unpack: 31.966s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 857.7113 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.183s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 642,292B, BPFP=0.4077 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 642,120B, BPFP=0.4076 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,266,984B, BPFP=0.8042 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,262,032B, BPFP=0.8011 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,696,484B, BPFP=1.0768 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,677,284B, BPFP=1.0647 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,221,904B, BPFP=0.7756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,180,344B, BPFP=0.7492 +⌛️ [2/4] FRONTEND: Frontend time: 32.326s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.648s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14589856 4.35586801 + layer.9.1 0.03321603 0.33645196 + layer.19.0 0.11866178 19.22503402 + layer.19.1 0.11267978 27.33467054 + layer.29.0 0.10803594 67.22145149 + layer.29.1 0.10714094 31.01129855 + layer.39.0 11.58943751 2618.61553461 + layer.39.1 9.70079103 3070.99025024 + ------------------------------------------------------------------------------------- + TOTAL 2.73948269 729.88631993 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9589444 +BPFP 0.7609 bits/point +EBPFP 0.7609 equivalent bits/point +MSE 729.886320 +---------------------- ---------------------------------------------------------- +Time: 65.157s Load: 1.183s, Pack+Encode: 32.326s, Decode+Unpack: 31.648s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 729.8863 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.166s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 600,732B, BPFP=0.3813 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 603,144B, BPFP=0.3828 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,244,476B, BPFP=0.7899 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,254,748B, BPFP=0.7965 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,634,916B, BPFP=1.0378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,650,952B, BPFP=1.0479 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,201,588B, BPFP=0.7627 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,185,364B, BPFP=0.7524 +⌛️ [2/4] FRONTEND: Frontend time: 32.747s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.874s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03308301 4.33738505 + layer.9.1 0.14566304 4.33694421 + layer.19.0 0.03810260 22.13523724 + layer.19.1 0.03780774 12.78943446 + layer.29.0 0.11592613 70.79008572 + layer.29.1 0.11717217 47.68941948 + layer.39.0 9.98032847 3094.10009750 + layer.39.1 9.70849498 3131.34351641 + ------------------------------------------------------------------------------------- + TOTAL 2.52207227 798.44026501 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9375920 +BPFP 0.7439 bits/point +EBPFP 0.7439 equivalent bits/point +MSE 798.440265 +---------------------- ---------------------------------------------------------- +Time: 65.788s Load: 1.166s, Pack+Encode: 32.747s, Decode+Unpack: 31.874s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 798.4403 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.155s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 581,968B, BPFP=0.3694 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 579,004B, BPFP=0.3675 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,296,908B, BPFP=0.8232 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,275,240B, BPFP=0.8095 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,632,852B, BPFP=1.0365 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,617,664B, BPFP=1.0268 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,179,684B, BPFP=0.7488 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,153,032B, BPFP=0.7319 +⌛️ [2/4] FRONTEND: Frontend time: 32.352s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.890s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14555504 0.60401521 + layer.9.1 0.14557384 0.47010194 + layer.19.0 0.03995539 18.46396526 + layer.19.1 0.04542811 32.22218019 + layer.29.0 0.12033866 46.83058783 + layer.29.1 0.13252172 51.77589779 + layer.39.0 10.37566776 3146.05362366 + layer.39.1 9.84188447 2973.49236269 + ------------------------------------------------------------------------------------- + TOTAL 2.60586562 783.73909182 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9316352 +BPFP 0.7392 bits/point +EBPFP 0.7392 equivalent bits/point +MSE 783.739092 +---------------------- ---------------------------------------------------------- +Time: 65.397s Load: 1.155s, Pack+Encode: 32.352s, Decode+Unpack: 31.890s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 783.7391 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,832B, BPFP=0.4379 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 689,664B, BPFP=0.4378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,323,660B, BPFP=0.8402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,318,840B, BPFP=0.8371 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,718,816B, BPFP=1.0910 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,723,552B, BPFP=1.0940 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,250,788B, BPFP=0.7939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,275,304B, BPFP=0.8095 +⌛️ [2/4] FRONTEND: Frontend time: 33.321s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.942s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14470203 4.30353162 + layer.9.1 0.14481130 8.39613145 + layer.19.0 0.11257574 37.92230663 + layer.19.1 0.11422884 13.50833680 + layer.29.0 0.10456927 46.13949768 + layer.29.1 0.10551051 32.41678685 + layer.39.0 10.36536069 2756.64023399 + layer.39.1 11.81531702 2760.27136822 + ------------------------------------------------------------------------------------- + TOTAL 2.86338443 707.44977416 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9990456 +BPFP 0.7927 bits/point +EBPFP 0.7927 equivalent bits/point +MSE 707.449774 +---------------------- ---------------------------------------------------------- +Time: 66.353s Load: 1.090s, Pack+Encode: 33.321s, Decode+Unpack: 31.942s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 707.4498 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.085s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 681,772B, BPFP=0.4328 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 681,236B, BPFP=0.4324 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,370,464B, BPFP=0.8699 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,343,880B, BPFP=0.8530 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,647,164B, BPFP=1.0455 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,628,200B, BPFP=1.0335 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,124,112B, BPFP=0.7135 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,137,372B, BPFP=0.7219 +⌛️ [2/4] FRONTEND: Frontend time: 33.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.936s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14530003 8.45547738 + layer.9.1 0.14546206 0.35711466 + layer.19.0 0.11891763 30.21792736 + layer.19.1 0.11677460 19.85415101 + layer.29.0 4.29725807 45.23903152 + layer.29.1 4.29692800 57.49071742 + layer.39.0 11.61914761 2562.88722782 + layer.39.1 11.22064282 2792.26259344 + ------------------------------------------------------------------------------------- + TOTAL 3.99505385 689.59553008 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9614200 +BPFP 0.7628 bits/point +EBPFP 0.7628 equivalent bits/point +MSE 689.595530 +---------------------- ---------------------------------------------------------- +Time: 66.600s Load: 1.085s, Pack+Encode: 33.579s, Decode+Unpack: 31.936s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 689.5955 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.970s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 582,644B, BPFP=0.3698 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 579,232B, BPFP=0.3677 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,129,944B, BPFP=0.7172 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,126,528B, BPFP=0.7151 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,439,200B, BPFP=0.9135 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,436,988B, BPFP=0.9121 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,094,628B, BPFP=0.6948 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,070,952B, BPFP=0.6798 +⌛️ [2/4] FRONTEND: Frontend time: 32.731s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.829s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67106792 3.04310110 + layer.9.1 2.67195307 0.34053158 + layer.19.0 0.08237472 6.69196927 + layer.19.1 0.08192194 22.84354688 + layer.29.0 0.11152953 103.88548099 + layer.29.1 0.11703055 109.18497116 + layer.39.0 163.01811830 2235.34449139 + layer.39.1 58.15221299 2380.40948976 + ------------------------------------------------------------------------------------- + TOTAL 28.36327613 607.71794777 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8460116 +BPFP 0.6713 bits/point +EBPFP 0.6713 equivalent bits/point +MSE 607.717948 +---------------------- ---------------------------------------------------------- +Time: 65.530s Load: 0.970s, Pack+Encode: 32.731s, Decode+Unpack: 31.829s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 607.7179 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.937s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 678,160B, BPFP=0.4305 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 678,500B, BPFP=0.4307 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,229,536B, BPFP=0.7804 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,240,268B, BPFP=0.7873 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,554,968B, BPFP=0.9870 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,600,296B, BPFP=1.0158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,072,228B, BPFP=0.6806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,071,548B, BPFP=0.6802 +⌛️ [2/4] FRONTEND: Frontend time: 33.854s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.911s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14620710 4.54600634 + layer.9.1 0.14642976 3.04644432 + layer.19.0 0.11726453 14.67931173 + layer.19.1 0.11958517 15.04566898 + layer.29.0 0.10693079 49.95850768 + layer.29.1 0.10826971 70.13375447 + layer.39.0 43.01306569 2900.32954176 + layer.39.1 17.12450997 2671.91452714 + ------------------------------------------------------------------------------------- + TOTAL 7.61028284 716.20672030 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9125504 +BPFP 0.7241 bits/point +EBPFP 0.7241 equivalent bits/point +MSE 716.206720 +---------------------- ---------------------------------------------------------- +Time: 66.701s Load: 0.937s, Pack+Encode: 33.854s, Decode+Unpack: 31.911s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 716.2067 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 584,448B, BPFP=0.3710 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 577,820B, BPFP=0.3668 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,156,100B, BPFP=0.7338 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,158,344B, BPFP=0.7353 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,439,452B, BPFP=0.9137 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,427,788B, BPFP=0.9063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 922,404B, BPFP=0.5855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 929,580B, BPFP=0.5901 +⌛️ [2/4] FRONTEND: Frontend time: 32.214s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.788s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03173930 0.33522472 + layer.9.1 0.03345565 3.01222909 + layer.19.0 3.26068347 14.18277492 + layer.19.1 3.26087326 13.72755271 + layer.29.0 4.24610771 40.54190110 + layer.29.1 4.24089229 33.11399344 + layer.39.0 8.81319124 2055.15550861 + layer.39.1 8.71779153 1828.97822554 + ------------------------------------------------------------------------------------- + TOTAL 4.07559181 498.63092627 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8195936 +BPFP 0.6503 bits/point +EBPFP 0.6503 equivalent bits/point +MSE 498.630926 +---------------------- ---------------------------------------------------------- +Time: 65.178s Load: 1.177s, Pack+Encode: 32.214s, Decode+Unpack: 31.788s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 498.6309 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 611,280B, BPFP=0.3880 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 610,900B, BPFP=0.3878 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,223,736B, BPFP=0.7768 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,224,280B, BPFP=0.7771 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,605,032B, BPFP=1.0188 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,625,388B, BPFP=1.0317 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 956,956B, BPFP=0.6074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 989,684B, BPFP=0.6282 +⌛️ [2/4] FRONTEND: Frontend time: 32.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078599 4.26547012 + layer.9.1 0.00079117 3.03130141 + layer.19.0 0.00795310 8.47455796 + layer.19.1 0.00811505 7.98086483 + layer.29.0 4.25797468 90.56780143 + layer.29.1 4.25504309 40.73970690 + layer.39.0 81.06806549 1803.43613910 + layer.39.1 44.82015254 1778.74195645 + ------------------------------------------------------------------------------------- + TOTAL 16.80236014 467.15472478 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8847256 +BPFP 0.7020 bits/point +EBPFP 0.7020 equivalent bits/point +MSE 467.154725 +---------------------- ---------------------------------------------------------- +Time: 65.244s Load: 1.175s, Pack+Encode: 32.571s, Decode+Unpack: 31.498s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 467.1547 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.105s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,644B, BPFP=0.4009 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 625,948B, BPFP=0.3973 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,286,564B, BPFP=0.8166 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,282,992B, BPFP=0.8144 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,666,808B, BPFP=1.0580 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,677,620B, BPFP=1.0649 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,067,132B, BPFP=0.6774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,102,892B, BPFP=0.7001 +⌛️ [2/4] FRONTEND: Frontend time: 33.817s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00082351 3.03127602 + layer.9.1 0.02968625 3.01851406 + layer.19.0 0.00841222 7.36531753 + layer.19.1 0.03743129 2.90189213 + layer.29.0 4.28408194 34.57126209 + layer.29.1 4.28564945 35.22879174 + layer.39.0 8.35370986 2268.33506662 + layer.39.1 8.52557915 2468.24536887 + ------------------------------------------------------------------------------------- + TOTAL 3.19067171 602.83718613 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9341600 +BPFP 0.7412 bits/point +EBPFP 0.7412 equivalent bits/point +MSE 602.837186 +---------------------- ---------------------------------------------------------- +Time: 66.915s Load: 1.105s, Pack+Encode: 33.817s, Decode+Unpack: 31.993s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 602.8372 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.114s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 632,972B, BPFP=0.4018 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 637,268B, BPFP=0.4045 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,248,364B, BPFP=0.7924 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,256,216B, BPFP=0.7974 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,646,340B, BPFP=1.0450 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,672,864B, BPFP=1.0619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,178,772B, BPFP=0.7482 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,176,868B, BPFP=0.7470 +⌛️ [2/4] FRONTEND: Frontend time: 33.729s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.957s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14529990 4.30439552 + layer.9.1 0.14524076 0.34215868 + layer.19.0 0.03780325 14.25995288 + layer.19.1 0.03783790 18.10393393 + layer.29.0 4.32098184 53.63381540 + layer.29.1 4.32100596 55.57242749 + layer.39.0 9.32673680 3505.08579786 + layer.39.1 9.31823369 3538.53071173 + ------------------------------------------------------------------------------------- + TOTAL 3.45664251 898.72914918 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9449664 +BPFP 0.7498 bits/point +EBPFP 0.7498 equivalent bits/point +MSE 898.729149 +---------------------- ---------------------------------------------------------- +Time: 66.799s Load: 1.114s, Pack+Encode: 33.729s, Decode+Unpack: 31.957s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 898.7291 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.116s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 653,000B, BPFP=0.4145 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 653,456B, BPFP=0.4148 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,287,356B, BPFP=0.8171 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,289,188B, BPFP=0.8183 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,726,132B, BPFP=1.0957 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,745,072B, BPFP=1.1077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,247,512B, BPFP=0.7919 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,237,988B, BPFP=0.7858 +⌛️ [2/4] FRONTEND: Frontend time: 33.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.892s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497286 0.34577096 + layer.9.1 0.14497296 4.19181884 + layer.19.0 0.03962668 83.22588967 + layer.19.1 0.11751332 41.27585209 + layer.29.0 0.14529291 47.18005565 + layer.29.1 0.16241527 80.17062581 + layer.39.0 11.40179406 3548.10724732 + layer.39.1 13.03458244 3575.45986350 + ------------------------------------------------------------------------------------- + TOTAL 3.14889631 922.49464048 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9839704 +BPFP 0.7807 bits/point +EBPFP 0.7807 equivalent bits/point +MSE 922.494640 +---------------------- ---------------------------------------------------------- +Time: 66.630s Load: 1.116s, Pack+Encode: 33.622s, Decode+Unpack: 31.892s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 922.4946 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.871s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 707,064B, BPFP=0.4488 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 708,920B, BPFP=0.4500 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,336,740B, BPFP=0.8485 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,338,740B, BPFP=0.8498 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,710,292B, BPFP=1.0856 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,738,380B, BPFP=1.1034 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,334,592B, BPFP=0.8471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,353,952B, BPFP=0.8594 +⌛️ [2/4] FRONTEND: Frontend time: 32.201s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.814s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14513344 4.30415209 + layer.9.1 0.03283094 0.34822463 + layer.19.0 0.11544709 7.66472454 + layer.19.1 0.11326018 52.43691095 + layer.29.0 0.14483232 130.97718963 + layer.29.1 0.14672551 44.74769967 + layer.39.0 10.02784076 3459.37049074 + layer.39.1 15.62606130 3485.33961651 + ------------------------------------------------------------------------------------- + TOTAL 3.29401644 898.14862610 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10228680 +BPFP 0.8116 bits/point +EBPFP 0.8116 equivalent bits/point +MSE 898.148626 +---------------------- ---------------------------------------------------------- +Time: 64.886s Load: 0.871s, Pack+Encode: 32.201s, Decode+Unpack: 31.814s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 898.1486 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.819s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 732,356B, BPFP=0.4649 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 731,284B, BPFP=0.4642 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,380,972B, BPFP=0.8766 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,384,184B, BPFP=0.8786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,785,852B, BPFP=1.1336 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,800,384B, BPFP=1.1428 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,323,792B, BPFP=0.8403 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,352,688B, BPFP=0.8586 +⌛️ [2/4] FRONTEND: Frontend time: 33.283s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.941s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492303 3.33892590 + layer.9.1 0.14484742 0.34832242 + layer.19.0 0.11740684 44.80542635 + layer.19.1 0.11489933 35.28573133 + layer.29.0 0.12072669 52.70119028 + layer.29.1 0.12118037 58.68392509 + layer.39.0 10.74778980 3641.09229769 + layer.39.1 11.83662176 3465.12544686 + ------------------------------------------------------------------------------------- + TOTAL 2.91854940 912.67265824 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10491512 +BPFP 0.8324 bits/point +EBPFP 0.8324 equivalent bits/point +MSE 912.672658 +---------------------- ---------------------------------------------------------- +Time: 66.044s Load: 0.819s, Pack+Encode: 33.283s, Decode+Unpack: 31.941s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 912.6727 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.819s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 770,956B, BPFP=0.4894 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 769,040B, BPFP=0.4881 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,449,340B, BPFP=0.9200 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,440,500B, BPFP=0.9144 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,876,024B, BPFP=1.1908 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,854,836B, BPFP=1.1774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,376,444B, BPFP=0.8737 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,403,672B, BPFP=0.8910 +⌛️ [2/4] FRONTEND: Frontend time: 33.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.959s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14465000 4.55941639 + layer.9.1 0.14489275 20.84365860 + layer.19.0 0.11978787 92.54539730 + layer.19.1 0.12819003 164.15913430 + layer.29.0 0.12519148 45.62562459 + layer.29.1 0.13018718 44.31379489 + layer.39.0 10.77894586 3205.10367241 + layer.39.1 10.25834823 3605.68410790 + ------------------------------------------------------------------------------------- + TOTAL 2.72877418 897.85435080 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10940812 +BPFP 0.8681 bits/point +EBPFP 0.8681 equivalent bits/point +MSE 897.854351 +---------------------- ---------------------------------------------------------- +Time: 66.079s Load: 0.819s, Pack+Encode: 33.300s, Decode+Unpack: 31.959s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 897.8544 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.039s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 626,192B, BPFP=0.3975 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 631,600B, BPFP=0.4009 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,228,364B, BPFP=0.7797 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,231,540B, BPFP=0.7817 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,516,196B, BPFP=0.9624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,525,328B, BPFP=0.9682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 972,964B, BPFP=0.6176 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 991,344B, BPFP=0.6293 +⌛️ [2/4] FRONTEND: Frontend time: 33.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.973s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14553812 20.91021211 + layer.9.1 0.14559401 4.43462585 + layer.19.0 0.04492324 14.90999122 + layer.19.1 0.04213941 11.18106745 + layer.29.0 4.25320263 56.50053319 + layer.29.1 4.25391672 75.24207325 + layer.39.0 8.72311137 1508.01917452 + layer.39.1 8.87262096 1634.30760481 + ------------------------------------------------------------------------------------- + TOTAL 3.31013081 415.68816030 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 8723528 +BPFP 0.6922 bits/point +EBPFP 0.6922 equivalent bits/point +MSE 415.688160 +---------------------- ---------------------------------------------------------- +Time: 66.592s Load: 1.039s, Pack+Encode: 33.580s, Decode+Unpack: 31.973s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 415.6882 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.096s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 678,616B, BPFP=0.4308 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 677,732B, BPFP=0.4302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,275,860B, BPFP=0.8099 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,271,016B, BPFP=0.8068 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,658,456B, BPFP=1.0527 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,657,048B, BPFP=1.0518 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,163,404B, BPFP=0.7385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,139,024B, BPFP=0.7230 +⌛️ [2/4] FRONTEND: Frontend time: 33.342s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14518122 0.35017367 + layer.9.1 0.14529820 0.34602701 + layer.19.0 0.11833418 75.03932402 + layer.19.1 0.12038008 17.88739285 + layer.29.0 4.31360161 64.98066298 + layer.29.1 4.31792870 133.28757719 + layer.39.0 9.40764201 2326.39047774 + layer.39.1 11.30764416 2363.92395190 + ------------------------------------------------------------------------------------- + TOTAL 3.73450127 622.77569842 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9521156 +BPFP 0.7554 bits/point +EBPFP 0.7554 equivalent bits/point +MSE 622.775698 +---------------------- ---------------------------------------------------------- +Time: 65.978s Load: 1.096s, Pack+Encode: 33.342s, Decode+Unpack: 31.540s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 622.7757 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.961s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 765,160B, BPFP=0.4857 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 764,580B, BPFP=0.4853 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,439,716B, BPFP=0.9139 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,468,032B, BPFP=0.9318 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,804,456B, BPFP=1.1454 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,849,188B, BPFP=1.1738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,100,728B, BPFP=0.6987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,116,736B, BPFP=0.7088 +⌛️ [2/4] FRONTEND: Frontend time: 33.663s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.108s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00315138 4.44747128 + layer.9.1 0.00505826 4.31632278 + layer.19.0 0.09147678 50.67423627 + layer.19.1 0.09143778 112.51196376 + layer.29.0 0.11015094 82.37686870 + layer.29.1 0.11338039 63.28041721 + layer.39.0 9.14784464 2106.82759181 + layer.39.1 8.98944348 2127.56191095 + ------------------------------------------------------------------------------------- + TOTAL 2.31899296 568.99959785 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10308596 +BPFP 0.8179 bits/point +EBPFP 0.8179 equivalent bits/point +MSE 568.999598 +---------------------- ---------------------------------------------------------- +Time: 66.732s Load: 0.961s, Pack+Encode: 33.663s, Decode+Unpack: 32.108s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 568.9996 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.876s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 802,232B, BPFP=0.5092 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 801,652B, BPFP=0.5088 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,508,644B, BPFP=0.9576 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,503,388B, BPFP=0.9543 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,946,840B, BPFP=1.2358 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,905,148B, BPFP=1.2093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,437,520B, BPFP=0.9125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,385,432B, BPFP=0.8794 +⌛️ [2/4] FRONTEND: Frontend time: 33.830s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14591913 4.35372382 + layer.9.1 0.03347605 0.49367718 + layer.19.0 0.12173996 69.67723737 + layer.19.1 0.12099332 117.74307361 + layer.29.0 0.11078974 30.77758115 + layer.29.1 0.11776269 61.90738239 + layer.39.0 10.17800795 3048.99512512 + layer.39.1 9.88744998 3095.56353591 + ------------------------------------------------------------------------------------- + TOTAL 2.58951735 803.68891707 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 11290856 +BPFP 0.8959 bits/point +EBPFP 0.8959 equivalent bits/point +MSE 803.688917 +---------------------- ---------------------------------------------------------- +Time: 66.743s Load: 0.876s, Pack+Encode: 33.830s, Decode+Unpack: 32.037s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 803.6889 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.120s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 614,192B, BPFP=0.3899 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 616,960B, BPFP=0.3916 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,298,080B, BPFP=0.8240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,287,840B, BPFP=0.8175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,799,080B, BPFP=1.1420 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,774,080B, BPFP=1.1261 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,108,516B, BPFP=0.7036 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,117,036B, BPFP=0.7090 +⌛️ [2/4] FRONTEND: Frontend time: 33.802s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66405266 0.33552586 + layer.9.1 2.66543197 0.33497470 + layer.19.0 3.22131407 21.48947839 + layer.19.1 3.22426883 3.07133857 + layer.29.0 4.27224607 38.53658698 + layer.29.1 4.27784520 43.11717481 + layer.39.0 8.94937744 2676.69304517 + layer.39.1 8.82170070 2813.36171596 + ------------------------------------------------------------------------------------- + TOTAL 4.76202962 699.61748006 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9615784 +BPFP 0.7630 bits/point +EBPFP 0.7630 equivalent bits/point +MSE 699.617480 +---------------------- ---------------------------------------------------------- +Time: 66.919s Load: 1.120s, Pack+Encode: 33.802s, Decode+Unpack: 31.997s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 699.6175 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.128s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 672,912B, BPFP=0.4271 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 671,488B, BPFP=0.4262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,298,684B, BPFP=0.8243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,301,144B, BPFP=0.8259 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,694,968B, BPFP=1.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,702,260B, BPFP=1.0805 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,064,060B, BPFP=0.6754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,080,632B, BPFP=0.6859 +⌛️ [2/4] FRONTEND: Frontend time: 33.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.937s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00104424 4.39887643 + layer.9.1 0.00091568 4.34569487 + layer.19.0 0.08171424 13.19012152 + layer.19.1 0.08373584 62.01153213 + layer.29.0 4.26071267 41.11766229 + layer.29.1 4.26438533 56.09263284 + layer.39.0 8.39843369 1962.70279493 + layer.39.1 8.51949380 2135.19012025 + ------------------------------------------------------------------------------------- + TOTAL 3.20130444 534.88117941 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9486148 +BPFP 0.7527 bits/point +EBPFP 0.7527 equivalent bits/point +MSE 534.881179 +---------------------- ---------------------------------------------------------- +Time: 66.672s Load: 1.128s, Pack+Encode: 33.607s, Decode+Unpack: 31.937s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 534.8812 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.969s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 731,328B, BPFP=0.4642 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 731,640B, BPFP=0.4644 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,421,396B, BPFP=0.9022 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,412,880B, BPFP=0.8968 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,860,976B, BPFP=1.1813 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,837,436B, BPFP=1.1663 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,202,436B, BPFP=0.7632 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,145,560B, BPFP=0.7271 +⌛️ [2/4] FRONTEND: Frontend time: 32.968s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14622840 3.03536984 + layer.9.1 0.03344178 0.62662179 + layer.19.0 0.12675888 89.55927039 + layer.19.1 0.12382618 133.24868988 + layer.29.0 0.12223263 47.39107694 + layer.29.1 0.12797405 42.17503351 + layer.39.0 10.69978368 2281.76178096 + layer.39.1 8.63538768 2477.50032499 + ------------------------------------------------------------------------------------- + TOTAL 2.50195416 634.41227104 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 10343652 +BPFP 0.8207 bits/point +EBPFP 0.8207 equivalent bits/point +MSE 634.412271 +---------------------- ---------------------------------------------------------- +Time: 65.530s Load: 0.969s, Pack+Encode: 32.968s, Decode+Unpack: 31.593s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 634.4123 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.076s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 716,008B, BPFP=0.4545 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 716,856B, BPFP=0.4550 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,320,512B, BPFP=0.8382 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,332,280B, BPFP=0.8457 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,715,456B, BPFP=1.0889 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,717,012B, BPFP=1.0899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,176,824B, BPFP=0.7470 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,187,976B, BPFP=0.7541 +⌛️ [2/4] FRONTEND: Frontend time: 33.821s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492994 0.48366840 + layer.9.1 0.14498602 4.41563478 + layer.19.0 0.12957112 31.23576129 + layer.19.1 0.13054295 121.76591445 + layer.29.0 0.16610158 312.29436139 + layer.29.1 0.14872770 167.00702795 + layer.39.0 16.52878844 3119.70848229 + layer.39.1 24.55764797 3591.80662983 + ------------------------------------------------------------------------------------- + TOTAL 5.24391197 918.58968505 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 9882924 +BPFP 0.7841 bits/point +EBPFP 0.7841 equivalent bits/point +MSE 918.589685 +---------------------- ---------------------------------------------------------- +Time: 66.893s Load: 1.076s, Pack+Encode: 33.821s, Decode+Unpack: 31.997s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 918.5897 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7535 bits/point +Avg EBPFP 0.7535 equivalent bits/point +Avg MSE 702.784805 +Avg Time 65.832s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..f703201b634aab58b61925971db800d560ca626e --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 520 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.202s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,338,092B, BPFP=0.8494 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,311,672B, BPFP=0.8326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,122,948B, BPFP=1.3475 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,092,036B, BPFP=1.3279 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,388,136B, BPFP=1.5159 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,403,588B, BPFP=1.5257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,293,000B, BPFP=0.8207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,319,356B, BPFP=0.8375 +⌛️ [2/4] FRONTEND: Frontend time: 33.683s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.906s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561056 6.91576173 + layer.9.1 0.14522085 4.19303819 + layer.19.0 3.25142184 5.11172929 + layer.19.1 3.25206135 5.38340155 + layer.29.0 4.23946030 52.83161866 + layer.29.1 4.24539299 49.49116428 + layer.39.0 32.17105490 701.91891453 + layer.39.1 19.15684032 770.87317192 + ------------------------------------------------------------------------------------- + TOTAL 8.32588289 199.58985002 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14268828 +BPFP 1.1321 bits/point +EBPFP 1.1321 equivalent bits/point +MSE 199.589850 +---------------------- ---------------------------------------------------------- +Time: 66.791s Load: 1.202s, Pack+Encode: 33.683s, Decode+Unpack: 31.906s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 199.5899 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00512-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00512-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,535,120B, BPFP=0.9744 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,560,688B, BPFP=0.9906 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,374,480B, BPFP=1.5072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,413,900B, BPFP=1.5322 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,733,376B, BPFP=1.7350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,769,044B, BPFP=1.7577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,675,368B, BPFP=1.0634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,606,928B, BPFP=1.0200 +⌛️ [2/4] FRONTEND: Frontend time: 33.733s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03307773 4.45107539 + layer.9.1 0.03291117 0.50587195 + layer.19.0 0.04156009 6.36988519 + layer.19.1 0.03760627 6.71960691 + layer.29.0 4.28582750 32.72644317 + layer.29.1 4.28551552 42.22610802 + layer.39.0 9.83402183 1307.28745531 + layer.39.1 9.85397836 1098.06499838 + ------------------------------------------------------------------------------------- + TOTAL 3.55056231 312.29393054 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16668904 +BPFP 1.3226 bits/point +EBPFP 1.3226 equivalent bits/point +MSE 312.293931 +---------------------- ---------------------------------------------------------- +Time: 67.006s Load: 1.215s, Pack+Encode: 33.733s, Decode+Unpack: 32.057s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 312.2939 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00657-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00657-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.099s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,587,024B, BPFP=1.0074 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,609,988B, BPFP=1.0219 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,372,780B, BPFP=1.5061 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,393,772B, BPFP=1.5194 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,774,668B, BPFP=1.7612 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,780,760B, BPFP=1.7651 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,502,372B, BPFP=0.9536 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,506,332B, BPFP=0.9561 +⌛️ [2/4] FRONTEND: Frontend time: 33.882s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083900 4.29057225 + layer.9.1 0.00259629 7.05750896 + layer.19.0 0.00955961 7.39067705 + layer.19.1 0.08538111 5.27037261 + layer.29.0 0.11631418 34.51483537 + layer.29.1 0.11200302 34.47126107 + layer.39.0 14.47657393 974.02193695 + layer.39.1 13.08093694 1171.91038349 + ------------------------------------------------------------------------------------- + TOTAL 3.48552551 279.86594347 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16527696 +BPFP 1.3114 bits/point +EBPFP 1.3114 equivalent bits/point +MSE 279.865943 +---------------------- ---------------------------------------------------------- +Time: 67.045s Load: 1.099s, Pack+Encode: 33.882s, Decode+Unpack: 32.064s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 279.8659 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00676-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00676-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,408,388B, BPFP=0.8940 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,374,028B, BPFP=0.8722 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,263,848B, BPFP=1.4370 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,196,516B, BPFP=1.3942 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,472,832B, BPFP=1.5696 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,434,188B, BPFP=1.5451 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,368,692B, BPFP=0.8688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,362,684B, BPFP=0.8650 +⌛️ [2/4] FRONTEND: Frontend time: 33.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.70169554 0.49516801 + layer.9.1 0.03294074 4.30590082 + layer.19.0 3.25671692 3.91571285 + layer.19.1 3.25834093 6.24737087 + layer.29.0 0.10810242 43.02194711 + layer.29.1 0.10661203 40.28343100 + layer.39.0 8.95005916 993.60716607 + layer.39.1 8.98756017 709.60220994 + ------------------------------------------------------------------------------------- + TOTAL 3.42525349 225.18486333 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14881176 +BPFP 1.1807 bits/point +EBPFP 1.1807 equivalent bits/point +MSE 225.184863 +---------------------- ---------------------------------------------------------- +Time: 66.611s Load: 1.091s, Pack+Encode: 33.511s, Decode+Unpack: 32.009s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 225.1849 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00688-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00688-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.111s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,520,736B, BPFP=0.9653 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,540,188B, BPFP=0.9776 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,356,140B, BPFP=1.4956 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,350,984B, BPFP=1.4923 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,698,440B, BPFP=1.7128 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,683,112B, BPFP=1.7031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,402,308B, BPFP=0.8901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,436,416B, BPFP=0.9118 +⌛️ [2/4] FRONTEND: Frontend time: 33.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.751s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527870 0.49706718 + layer.9.1 0.14521496 0.37813761 + layer.19.0 0.03964342 3.50720377 + layer.19.1 0.03956446 6.41068119 + layer.29.0 0.12258449 45.83076048 + layer.29.1 0.12735008 45.23478124 + layer.39.0 32.94776263 925.90875853 + layer.39.1 29.25669534 858.73895028 + ------------------------------------------------------------------------------------- + TOTAL 7.85301176 235.81329253 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15988324 +BPFP 1.2686 bits/point +EBPFP 1.2686 equivalent bits/point +MSE 235.813293 +---------------------- ---------------------------------------------------------- +Time: 66.393s Load: 1.111s, Pack+Encode: 33.531s, Decode+Unpack: 31.751s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 235.8133 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00693-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00693-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.040s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,434,372B, BPFP=0.9105 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,503,668B, BPFP=0.9545 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,277,008B, BPFP=1.4453 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,329,548B, BPFP=1.4787 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,474,904B, BPFP=1.5709 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,496,264B, BPFP=1.5845 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,307,060B, BPFP=0.8297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,313,216B, BPFP=0.8336 +⌛️ [2/4] FRONTEND: Frontend time: 33.689s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.918s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66816044 0.34092350 + layer.9.1 2.66817504 4.31990563 + layer.19.0 3.22262959 4.41942169 + layer.19.1 3.22037432 8.49200152 + layer.29.0 4.30448692 44.49693289 + layer.29.1 4.31085282 47.78256520 + layer.39.0 38.33931691 750.74463763 + layer.39.1 57.25219370 763.70693858 + ------------------------------------------------------------------------------------- + TOTAL 14.49827372 203.03791583 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15136040 +BPFP 1.2009 bits/point +EBPFP 1.2009 equivalent bits/point +MSE 203.037916 +---------------------- ---------------------------------------------------------- +Time: 66.647s Load: 1.040s, Pack+Encode: 33.689s, Decode+Unpack: 31.918s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 203.0379 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bathroom-rgb_00731-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bathroom-rgb_00731-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,563,364B, BPFP=0.9923 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,551,556B, BPFP=0.9848 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,280,220B, BPFP=1.4474 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,262,696B, BPFP=1.4362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,572,860B, BPFP=1.6331 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,535,608B, BPFP=1.6095 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,381,120B, BPFP=0.8767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,364,008B, BPFP=0.8658 +⌛️ [2/4] FRONTEND: Frontend time: 33.383s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.676s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332920 0.47101368 + layer.9.1 0.00092169 0.34153187 + layer.19.0 3.23006092 5.20037812 + layer.19.1 3.23257961 4.93242264 + layer.29.0 4.28548854 35.15878392 + layer.29.1 4.27808990 38.01010522 + layer.39.0 10.57841825 906.09010400 + layer.39.1 20.33118703 961.60895353 + ------------------------------------------------------------------------------------- + TOTAL 5.75625939 243.97666162 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15511432 +BPFP 1.2307 bits/point +EBPFP 1.2307 equivalent bits/point +MSE 243.976662 +---------------------- ---------------------------------------------------------- +Time: 66.110s Load: 1.051s, Pack+Encode: 33.383s, Decode+Unpack: 31.676s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 243.9767 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00061-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00061-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.960s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,665,960B, BPFP=1.0575 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,675,008B, BPFP=1.0632 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,401,044B, BPFP=1.5241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,394,180B, BPFP=1.5197 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,602,332B, BPFP=1.6518 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,581,968B, BPFP=1.6389 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,383,892B, BPFP=0.8784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,382,260B, BPFP=0.8774 +⌛️ [2/4] FRONTEND: Frontend time: 33.343s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.414s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14443954 4.19205972 + layer.9.1 0.14435121 8.37581121 + layer.19.0 0.03807715 10.83845873 + layer.19.1 0.03781311 9.53450055 + layer.29.0 0.10781899 50.51163369 + layer.29.1 0.10618912 49.35242322 + layer.39.0 9.30898666 1060.88950276 + layer.39.1 9.83625107 1093.20360741 + ------------------------------------------------------------------------------------- + TOTAL 2.46549086 285.86224966 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16086644 +BPFP 1.2764 bits/point +EBPFP 1.2764 equivalent bits/point +MSE 285.862250 +---------------------- ---------------------------------------------------------- +Time: 65.717s Load: 0.960s, Pack+Encode: 33.343s, Decode+Unpack: 31.414s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 285.8622 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00175-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00175-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.230s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,727,684B, BPFP=1.0966 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,720,828B, BPFP=1.0923 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,495,212B, BPFP=1.5838 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,500,064B, BPFP=1.5869 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,827,304B, BPFP=1.7946 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,846,040B, BPFP=1.8065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,659,012B, BPFP=1.0531 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,592,616B, BPFP=1.0109 +⌛️ [2/4] FRONTEND: Frontend time: 33.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.871s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14543928 4.51884572 + layer.9.1 0.14562574 3.05365222 + layer.19.0 0.11552505 13.02150278 + layer.19.1 0.12052174 7.87065704 + layer.29.0 0.10841144 38.49092816 + layer.29.1 0.10845811 39.86576820 + layer.39.0 9.17501701 1732.74325642 + layer.39.1 9.20635778 1544.79216770 + ------------------------------------------------------------------------------------- + TOTAL 2.39066952 423.04459728 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17368760 +BPFP 1.3781 bits/point +EBPFP 1.3781 equivalent bits/point +MSE 423.044597 +---------------------- ---------------------------------------------------------- +Time: 66.609s Load: 1.230s, Pack+Encode: 33.509s, Decode+Unpack: 31.871s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 423.0446 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00186-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00186-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.166s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,537,200B, BPFP=0.9757 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,548,192B, BPFP=0.9827 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,147,540B, BPFP=1.3632 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,196,472B, BPFP=1.3942 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,223,832B, BPFP=1.4116 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,295,080B, BPFP=1.4568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,248,220B, BPFP=0.7923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,270,620B, BPFP=0.8065 +⌛️ [2/4] FRONTEND: Frontend time: 33.007s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.618s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.78226592 4.26277053 + layer.9.1 2.78427046 4.30785141 + layer.19.0 3.22580366 9.02967963 + layer.19.1 3.22969594 9.77356128 + layer.29.0 4.29525448 40.20247502 + layer.29.1 0.11349234 43.39115819 + layer.39.0 8.89338553 1030.45060123 + layer.39.1 8.88767087 848.73789405 + ------------------------------------------------------------------------------------- + TOTAL 4.27647990 248.76949892 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14467156 +BPFP 1.1479 bits/point +EBPFP 1.1479 equivalent bits/point +MSE 248.769499 +---------------------- ---------------------------------------------------------- +Time: 65.792s Load: 1.166s, Pack+Encode: 33.007s, Decode+Unpack: 31.618s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 248.7695 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00282-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00282-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.143s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,497,436B, BPFP=0.9505 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,451,064B, BPFP=0.9211 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,287,184B, BPFP=1.4518 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,215,688B, BPFP=1.4064 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,393,132B, BPFP=1.5190 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,343,276B, BPFP=1.4874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,227,972B, BPFP=0.7795 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,239,236B, BPFP=0.7866 +⌛️ [2/4] FRONTEND: Frontend time: 32.923s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.702s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14514284 16.24963058 + layer.9.1 0.14518188 2.98373359 + layer.19.0 0.04057091 4.68770756 + layer.19.1 0.04041447 8.52252981 + layer.29.0 4.25641542 32.18202846 + layer.29.1 4.26613502 30.27316329 + layer.39.0 12.58558458 891.08661033 + layer.39.1 8.96866240 947.00471238 + ------------------------------------------------------------------------------------- + TOTAL 3.80601344 241.62376450 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14654988 +BPFP 1.1628 bits/point +EBPFP 1.1628 equivalent bits/point +MSE 241.623764 +---------------------- ---------------------------------------------------------- +Time: 65.768s Load: 1.143s, Pack+Encode: 32.923s, Decode+Unpack: 31.702s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 241.6238 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00524-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00524-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.224s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,581,520B, BPFP=1.0039 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,522,408B, BPFP=0.9663 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,299,664B, BPFP=1.4597 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,254,504B, BPFP=1.4310 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,590,980B, BPFP=1.6446 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,555,832B, BPFP=1.6223 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,347,084B, BPFP=0.8551 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,367,780B, BPFP=0.8682 +⌛️ [2/4] FRONTEND: Frontend time: 33.327s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67015388 3.12416467 + layer.9.1 0.00076871 4.33638246 + layer.19.0 3.22151687 4.32702815 + layer.19.1 3.22388957 9.14809727 + layer.29.0 4.24084786 55.32689105 + layer.29.1 4.24602234 53.49238808 + layer.39.0 7.87160790 667.43114235 + layer.39.1 9.85764150 654.77689308 + ------------------------------------------------------------------------------------- + TOTAL 4.41655608 181.49537339 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15519772 +BPFP 1.2314 bits/point +EBPFP 1.2314 equivalent bits/point +MSE 181.495373 +---------------------- ---------------------------------------------------------- +Time: 66.548s Load: 1.224s, Pack+Encode: 33.327s, Decode+Unpack: 31.998s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 181.4954 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00538-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00538-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,661,220B, BPFP=1.0545 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,694,788B, BPFP=1.0758 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,399,844B, BPFP=1.5233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,444,096B, BPFP=1.5514 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,726,536B, BPFP=1.7307 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,743,360B, BPFP=1.7413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,512,412B, BPFP=0.9600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,518,324B, BPFP=0.9638 +⌛️ [2/4] FRONTEND: Frontend time: 33.403s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.745s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00068237 4.33120608 + layer.9.1 0.00070576 4.37279615 + layer.19.0 0.00823322 6.08608921 + layer.19.1 0.08594799 6.98896678 + layer.29.0 0.12200666 34.91282855 + layer.29.1 0.12451052 35.54301064 + layer.39.0 55.99513528 1134.35781605 + layer.39.1 28.81185256 1356.48610660 + ------------------------------------------------------------------------------------- + TOTAL 10.64363429 322.88485251 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16700580 +BPFP 1.3251 bits/point +EBPFP 1.3251 equivalent bits/point +MSE 322.884853 +---------------------- ---------------------------------------------------------- +Time: 66.357s Load: 1.208s, Pack+Encode: 33.403s, Decode+Unpack: 31.745s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 322.8849 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00927-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00927-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,571,928B, BPFP=0.9978 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,576,924B, BPFP=1.0010 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,273,616B, BPFP=1.4432 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,270,084B, BPFP=1.4409 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,460,664B, BPFP=1.5619 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,483,988B, BPFP=1.5767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,335,448B, BPFP=0.8477 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,351,504B, BPFP=0.8579 +⌛️ [2/4] FRONTEND: Frontend time: 32.933s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.625s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14556937 0.32343313 + layer.9.1 0.03327741 3.00092039 + layer.19.0 0.11590617 3.87173612 + layer.19.1 0.11733878 7.13135067 + layer.29.0 0.11334742 37.55068096 + layer.29.1 4.29039579 38.76914659 + layer.39.0 9.10722066 867.21100097 + layer.39.1 44.52401893 799.41826454 + ------------------------------------------------------------------------------------- + TOTAL 7.30588432 219.65956667 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15324156 +BPFP 1.2159 bits/point +EBPFP 1.2159 equivalent bits/point +MSE 219.659567 +---------------------- ---------------------------------------------------------- +Time: 65.771s Load: 1.213s, Pack+Encode: 32.933s, Decode+Unpack: 31.625s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 219.6596 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_00933-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_00933-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.232s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,777,908B, BPFP=1.1285 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,768,968B, BPFP=1.1229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,469,912B, BPFP=1.5678 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,428,712B, BPFP=1.5416 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,649,176B, BPFP=1.6816 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,636,976B, BPFP=1.6738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,247,080B, BPFP=0.7916 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,265,668B, BPFP=0.8034 +⌛️ [2/4] FRONTEND: Frontend time: 33.683s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.807s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11318631 8.25084676 + layer.9.1 0.11319129 0.48805885 + layer.19.0 0.00665199 6.37760184 + layer.19.1 0.00853768 2.75137836 + layer.29.0 4.27225940 38.12274283 + layer.29.1 4.27324961 35.84938150 + layer.39.0 14.80262837 811.23277543 + layer.39.1 16.56649765 859.85545986 + ------------------------------------------------------------------------------------- + TOTAL 5.01952529 220.36603068 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16244400 +BPFP 1.2889 bits/point +EBPFP 1.2889 equivalent bits/point +MSE 220.366031 +---------------------- ---------------------------------------------------------- +Time: 66.722s Load: 1.232s, Pack+Encode: 33.683s, Decode+Unpack: 31.807s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 220.3660 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01095-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01095-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.208s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,596,040B, BPFP=1.0131 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,622,672B, BPFP=1.0300 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,246,592B, BPFP=1.4260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,332,972B, BPFP=1.4809 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,505,768B, BPFP=1.5905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,517,040B, BPFP=1.5977 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,362,808B, BPFP=0.8650 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,352,360B, BPFP=0.8584 +⌛️ [2/4] FRONTEND: Frontend time: 33.368s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.908s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00065117 4.35373176 + layer.9.1 0.00066201 3.04765384 + layer.19.0 0.00984582 5.86785018 + layer.19.1 0.01156107 3.98470221 + layer.29.0 4.26547583 31.60008226 + layer.29.1 4.26296603 35.21776487 + layer.39.0 11.21169412 967.36130972 + layer.39.1 9.31977106 847.21928827 + ------------------------------------------------------------------------------------- + TOTAL 3.63532839 237.33154789 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15536252 +BPFP 1.2327 bits/point +EBPFP 1.2327 equivalent bits/point +MSE 237.331548 +---------------------- ---------------------------------------------------------- +Time: 66.484s Load: 1.208s, Pack+Encode: 33.368s, Decode+Unpack: 31.908s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 237.3315 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01125-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01125-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.180s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,559,192B, BPFP=0.9897 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,572,140B, BPFP=0.9979 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,213,180B, BPFP=1.4048 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,225,864B, BPFP=1.4129 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,288,324B, BPFP=1.4525 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,300,912B, BPFP=1.4605 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,219,704B, BPFP=0.7742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,221,032B, BPFP=0.7750 +⌛️ [2/4] FRONTEND: Frontend time: 33.215s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00085355 4.36775941 + layer.9.1 0.00085581 0.37209773 + layer.19.0 0.00808159 8.11867218 + layer.19.1 0.00635426 13.22402350 + layer.29.0 4.24551200 29.27814227 + layer.29.1 4.24803037 31.05806945 + layer.39.0 9.19283951 748.45653234 + layer.39.1 9.46657027 746.85074748 + ------------------------------------------------------------------------------------- + TOTAL 3.39613717 197.71575554 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14600348 +BPFP 1.1584 bits/point +EBPFP 1.1584 equivalent bits/point +MSE 197.715756 +---------------------- ---------------------------------------------------------- +Time: 65.988s Load: 1.180s, Pack+Encode: 33.215s, Decode+Unpack: 31.593s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 197.7158 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01135-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01135-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,491,340B, BPFP=0.9466 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,506,812B, BPFP=0.9564 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,361,332B, BPFP=1.4989 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,358,112B, BPFP=1.4968 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,733,228B, BPFP=1.7349 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,702,752B, BPFP=1.7156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,499,780B, BPFP=0.9520 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,497,660B, BPFP=0.9506 +⌛️ [2/4] FRONTEND: Frontend time: 33.063s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.772s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67056234 0.35553032 + layer.9.1 2.67147828 0.34121505 + layer.19.0 0.00618387 3.27499105 + layer.19.1 0.08383032 7.86266173 + layer.29.0 4.28489822 43.74184981 + layer.29.1 4.28470970 42.91987427 + layer.39.0 10.15376305 1320.43825154 + layer.39.1 8.47863686 1088.33449789 + ------------------------------------------------------------------------------------- + TOTAL 4.07925783 313.40860896 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16151016 +BPFP 1.2815 bits/point +EBPFP 1.2815 equivalent bits/point +MSE 313.408609 +---------------------- ---------------------------------------------------------- +Time: 65.907s Load: 1.072s, Pack+Encode: 33.063s, Decode+Unpack: 31.772s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 313.4086 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01161-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01161-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,506,504B, BPFP=0.9563 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,467,908B, BPFP=0.9318 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,321,844B, BPFP=1.4738 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,290,376B, BPFP=1.4538 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,609,280B, BPFP=1.6562 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,604,288B, BPFP=1.6531 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,370,320B, BPFP=0.8698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,367,756B, BPFP=0.8682 +⌛️ [2/4] FRONTEND: Frontend time: 33.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.758s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67132404 0.47442669 + layer.9.1 2.67117709 0.47109219 + layer.19.0 0.00597838 7.74395655 + layer.19.1 0.00605309 2.70027593 + layer.29.0 4.29273040 38.72327196 + layer.29.1 4.29206328 36.71516747 + layer.39.0 9.96127074 745.70011375 + layer.39.1 10.21295854 804.92687683 + ------------------------------------------------------------------------------------- + TOTAL 4.26419445 204.68189767 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15538276 +BPFP 1.2329 bits/point +EBPFP 1.2329 equivalent bits/point +MSE 204.681898 +---------------------- ---------------------------------------------------------- +Time: 66.484s Load: 1.080s, Pack+Encode: 33.646s, Decode+Unpack: 31.758s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 204.6819 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01162-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01162-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.096s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,515,836B, BPFP=0.9622 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,550,304B, BPFP=0.9841 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,260,560B, BPFP=1.4349 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,259,660B, BPFP=1.4343 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,319,948B, BPFP=1.4726 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,287,980B, BPFP=1.4523 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,244,752B, BPFP=0.7901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,255,124B, BPFP=0.7967 +⌛️ [2/4] FRONTEND: Frontend time: 33.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.731s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568686 0.44253880 + layer.9.1 0.14558674 4.28723156 + layer.19.0 0.00960369 5.70566908 + layer.19.1 0.03847206 8.66451127 + layer.29.0 4.24438723 35.82087616 + layer.29.1 4.24578970 35.61977982 + layer.39.0 9.23757985 731.65640234 + layer.39.1 9.43674592 898.75633734 + ------------------------------------------------------------------------------------- + TOTAL 3.43798151 215.11916830 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14694164 +BPFP 1.1659 bits/point +EBPFP 1.1659 equivalent bits/point +MSE 215.119168 +---------------------- ---------------------------------------------------------- +Time: 66.426s Load: 1.096s, Pack+Encode: 33.598s, Decode+Unpack: 31.731s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 215.1192 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01163-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01163-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.207s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,703,508B, BPFP=1.0813 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,719,592B, BPFP=1.0915 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,527,472B, BPFP=1.6043 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,485,760B, BPFP=1.5778 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,834,192B, BPFP=1.7990 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,815,620B, BPFP=1.7872 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,464,664B, BPFP=0.9297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,446,424B, BPFP=0.9181 +⌛️ [2/4] FRONTEND: Frontend time: 33.808s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.762s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11307967 4.40591612 + layer.9.1 0.00073224 3.09340850 + layer.19.0 0.08207503 7.91285648 + layer.19.1 0.08214869 7.32073334 + layer.29.0 4.26728487 39.70178441 + layer.29.1 4.26774951 37.86113960 + layer.39.0 12.81553410 1284.88032174 + layer.39.1 23.05196315 1092.29606760 + ------------------------------------------------------------------------------------- + TOTAL 5.58507091 309.68402847 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16997232 +BPFP 1.3486 bits/point +EBPFP 1.3486 equivalent bits/point +MSE 309.684028 +---------------------- ---------------------------------------------------------- +Time: 66.777s Load: 1.207s, Pack+Encode: 33.808s, Decode+Unpack: 31.762s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 309.6840 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bedroom-rgb_01195-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bedroom-rgb_01195-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.196s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,853,712B, BPFP=1.1766 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,868,876B, BPFP=1.1863 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,604,140B, BPFP=1.6530 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,624,448B, BPFP=1.6659 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,998,744B, BPFP=1.9035 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,010,928B, BPFP=1.9112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,626,000B, BPFP=1.0321 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,644,416B, BPFP=1.0438 +⌛️ [2/4] FRONTEND: Frontend time: 34.025s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.282s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519844 4.22294252 + layer.9.1 0.14499054 0.46211036 + layer.19.0 0.12156012 6.96278907 + layer.19.1 0.12030756 6.84744551 + layer.29.0 0.12020218 22.23200104 + layer.29.1 0.12115470 21.29882495 + layer.39.0 8.85439666 1425.64982125 + layer.39.1 8.75438231 1618.54972376 + ------------------------------------------------------------------------------------- + TOTAL 2.29777406 388.27820731 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 18231264 +BPFP 1.4465 bits/point +EBPFP 1.4465 equivalent bits/point +MSE 388.278207 +---------------------- ---------------------------------------------------------- +Time: 67.503s Load: 1.196s, Pack+Encode: 34.025s, Decode+Unpack: 32.282s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 388.2782 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00083-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00083-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.216s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,938,304B, BPFP=1.2303 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,921,832B, BPFP=1.2199 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,639,172B, BPFP=1.6752 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,656,868B, BPFP=1.6864 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,963,480B, BPFP=1.8811 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,951,288B, BPFP=1.8733 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,506,396B, BPFP=0.9562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,489,220B, BPFP=0.9453 +⌛️ [2/4] FRONTEND: Frontend time: 33.425s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14541288 4.18724229 + layer.9.1 0.14479464 0.47049981 + layer.19.0 0.11855170 6.11672794 + layer.19.1 0.11778439 9.61634328 + layer.29.0 0.12648388 22.84829989 + layer.29.1 0.12520221 21.71434484 + layer.39.0 8.37129624 1077.43069548 + layer.39.1 8.45478741 1214.40583360 + ------------------------------------------------------------------------------------- + TOTAL 2.20053917 294.59874839 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 18066560 +BPFP 1.4335 bits/point +EBPFP 1.4335 equivalent bits/point +MSE 294.598748 +---------------------- ---------------------------------------------------------- +Time: 66.619s Load: 1.216s, Pack+Encode: 33.425s, Decode+Unpack: 31.978s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 294.5987 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00084-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00084-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,891,180B, BPFP=1.2004 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,919,920B, BPFP=1.2187 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,717,528B, BPFP=1.7250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,707,572B, BPFP=1.7186 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,153,644B, BPFP=2.0018 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,154,008B, BPFP=2.0020 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,994,472B, BPFP=1.2660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,981,776B, BPFP=1.2579 +⌛️ [2/4] FRONTEND: Frontend time: 33.921s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.103s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14458197 8.24296126 + layer.9.1 0.14461228 4.23173190 + layer.19.0 0.12127609 6.02774682 + layer.19.1 0.12505172 5.81181003 + layer.29.0 0.11568762 17.02012665 + layer.29.1 0.11796058 17.33311159 + layer.39.0 8.63782956 2407.14364641 + layer.39.1 8.69862780 2567.12495938 + ------------------------------------------------------------------------------------- + TOTAL 2.26320345 629.11701175 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 19520100 +BPFP 1.5488 bits/point +EBPFP 1.5488 equivalent bits/point +MSE 629.117012 +---------------------- ---------------------------------------------------------- +Time: 67.250s Load: 1.227s, Pack+Encode: 33.921s, Decode+Unpack: 32.103s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 629.1170 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00085-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00085-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,902,688B, BPFP=1.2077 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,915,820B, BPFP=1.2161 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,725,576B, BPFP=1.7301 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,726,700B, BPFP=1.7308 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,144,520B, BPFP=1.9960 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,140,900B, BPFP=1.9937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 2,041,064B, BPFP=1.2956 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 2,021,964B, BPFP=1.2834 +⌛️ [2/4] FRONTEND: Frontend time: 33.084s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.932s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14478087 0.45895470 + layer.9.1 0.14472154 0.33764565 + layer.19.0 0.13423899 1.39993281 + layer.19.1 0.13534726 1.36103725 + layer.29.0 0.11251127 17.07608238 + layer.29.1 0.11242151 16.87158251 + layer.39.0 10.58490794 2353.43873903 + layer.39.1 8.80008176 2522.39811505 + ------------------------------------------------------------------------------------- + TOTAL 2.52112639 614.16776117 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 19619232 +BPFP 1.5567 bits/point +EBPFP 1.5567 equivalent bits/point +MSE 614.167761 +---------------------- ---------------------------------------------------------- +Time: 66.229s Load: 1.213s, Pack+Encode: 33.084s, Decode+Unpack: 31.932s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 614.1678 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00086-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00086-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.234s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,821,580B, BPFP=1.1562 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,845,316B, BPFP=1.1713 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,532,312B, BPFP=1.6074 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,545,088B, BPFP=1.6155 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,921,208B, BPFP=1.8542 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,922,032B, BPFP=1.8548 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,734,160B, BPFP=1.1008 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,709,868B, BPFP=1.0853 +⌛️ [2/4] FRONTEND: Frontend time: 33.963s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.260s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14590075 0.47836455 + layer.9.1 0.14620647 4.27202106 + layer.19.0 0.11628058 5.19254023 + layer.19.1 0.11601873 11.19047063 + layer.29.0 0.11558260 23.88462281 + layer.29.1 0.11828149 24.18411298 + layer.39.0 28.43028163 1861.85424114 + layer.39.1 24.81181701 1951.42541436 + ------------------------------------------------------------------------------------- + TOTAL 6.75004616 485.31022347 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 18031564 +BPFP 1.4307 bits/point +EBPFP 1.4307 equivalent bits/point +MSE 485.310223 +---------------------- ---------------------------------------------------------- +Time: 67.457s Load: 1.234s, Pack+Encode: 33.963s, Decode+Unpack: 32.260s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 485.3102 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00090-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00090-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,774,008B, BPFP=1.1261 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,789,408B, BPFP=1.1358 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,586,180B, BPFP=1.6416 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,578,668B, BPFP=1.6368 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,037,932B, BPFP=1.9283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,018,856B, BPFP=1.9162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,845,104B, BPFP=1.1712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,814,980B, BPFP=1.1521 +⌛️ [2/4] FRONTEND: Frontend time: 33.947s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11529700 4.31257903 + layer.9.1 0.14629077 0.36342609 + layer.19.0 0.09721754 6.38644835 + layer.19.1 0.12446257 1.60336836 + layer.29.0 4.28687864 21.38023034 + layer.29.1 4.28715508 16.33636532 + layer.39.0 11.34089363 2010.72456939 + layer.39.1 19.75513766 2149.08189795 + ------------------------------------------------------------------------------------- + TOTAL 5.01916661 526.27361060 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 18445136 +BPFP 1.4635 bits/point +EBPFP 1.4635 equivalent bits/point +MSE 526.273611 +---------------------- ---------------------------------------------------------- +Time: 67.227s Load: 1.227s, Pack+Encode: 33.947s, Decode+Unpack: 32.052s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 526.2736 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/bookstore-rgb_00116-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/bookstore-rgb_00116-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.216s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,664,412B, BPFP=1.0565 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,689,068B, BPFP=1.0721 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,427,236B, BPFP=1.5407 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,463,176B, BPFP=1.5635 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,824,008B, BPFP=1.7925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,859,920B, BPFP=1.8153 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,762,900B, BPFP=1.1190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,732,996B, BPFP=1.1000 +⌛️ [2/4] FRONTEND: Frontend time: 33.813s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.269s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14538718 4.45489341 + layer.9.1 0.14538559 3.01613597 + layer.19.0 0.11434236 5.64857524 + layer.19.1 0.11406084 2.12920315 + layer.29.0 0.11219077 24.08267743 + layer.29.1 0.11281304 31.78285973 + layer.39.0 79.88316542 1700.54809880 + layer.39.1 46.71980622 1573.95011375 + ------------------------------------------------------------------------------------- + TOTAL 15.91839393 418.20156968 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17423716 +BPFP 1.3825 bits/point +EBPFP 1.3825 equivalent bits/point +MSE 418.201570 +---------------------- ---------------------------------------------------------- +Time: 67.298s Load: 1.216s, Pack+Encode: 33.813s, Decode+Unpack: 32.269s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 418.2016 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00314-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00314-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.226s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,793,256B, BPFP=1.1383 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,786,776B, BPFP=1.1342 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,501,920B, BPFP=1.5881 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,505,828B, BPFP=1.5906 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,880,208B, BPFP=1.8282 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,870,864B, BPFP=1.8223 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,822,324B, BPFP=1.1567 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,798,452B, BPFP=1.1416 +⌛️ [2/4] FRONTEND: Frontend time: 33.895s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.962s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14512126 0.47565159 + layer.9.1 0.14517278 4.49057206 + layer.19.0 0.11689420 6.68230267 + layer.19.1 0.12099910 5.59005766 + layer.29.0 0.11847120 22.65090033 + layer.29.1 0.12399357 29.27777919 + layer.39.0 75.86630139 2285.40721482 + layer.39.1 56.61936342 2212.22749431 + ------------------------------------------------------------------------------------- + TOTAL 16.65703962 570.85024658 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17959628 +BPFP 1.4250 bits/point +EBPFP 1.4250 equivalent bits/point +MSE 570.850247 +---------------------- ---------------------------------------------------------- +Time: 67.083s Load: 1.226s, Pack+Encode: 33.895s, Decode+Unpack: 31.962s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 570.8502 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00316-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00316-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.222s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,730,400B, BPFP=1.0984 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,705,568B, BPFP=1.0826 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,501,364B, BPFP=1.5877 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,488,508B, BPFP=1.5796 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,891,988B, BPFP=1.8357 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,883,304B, BPFP=1.8302 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,858,012B, BPFP=1.1794 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,808,856B, BPFP=1.1482 +⌛️ [2/4] FRONTEND: Frontend time: 33.363s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488649 3.06628311 + layer.9.1 0.14606862 4.31087250 + layer.19.0 0.08767178 2.47554452 + layer.19.1 0.11443626 2.21056744 + layer.29.0 0.10933029 32.82840683 + layer.29.1 0.10817130 34.80310063 + layer.39.0 52.66717785 2143.55719857 + layer.39.1 62.91127214 1655.78924277 + ------------------------------------------------------------------------------------- + TOTAL 14.53237684 484.88015205 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17868000 +BPFP 1.4177 bits/point +EBPFP 1.4177 equivalent bits/point +MSE 484.880152 +---------------------- ---------------------------------------------------------- +Time: 66.655s Load: 1.222s, Pack+Encode: 33.363s, Decode+Unpack: 32.069s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 484.8802 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00324-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00324-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.191s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,761,996B, BPFP=1.1184 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,792,472B, BPFP=1.1378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,518,724B, BPFP=1.5988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,514,480B, BPFP=1.5961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,880,680B, BPFP=1.8285 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,862,056B, BPFP=1.8167 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,809,328B, BPFP=1.1485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,818,332B, BPFP=1.1542 +⌛️ [2/4] FRONTEND: Frontend time: 33.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.790s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509552 0.36562312 + layer.9.1 0.14520687 4.23973578 + layer.19.0 0.12118574 6.10937563 + layer.19.1 0.11709642 7.23660868 + layer.29.0 0.10963326 33.22997238 + layer.29.1 0.10842036 35.10325093 + layer.39.0 53.79489966 1969.57101072 + layer.39.1 62.27410526 1906.16233344 + ------------------------------------------------------------------------------------- + TOTAL 14.60195539 495.25223884 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17958068 +BPFP 1.4249 bits/point +EBPFP 1.4249 equivalent bits/point +MSE 495.252239 +---------------------- ---------------------------------------------------------- +Time: 66.562s Load: 1.191s, Pack+Encode: 33.580s, Decode+Unpack: 31.790s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 495.2522 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00325-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00325-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.154s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,924,176B, BPFP=1.2214 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,923,360B, BPFP=1.2209 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,602,732B, BPFP=1.6521 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,609,020B, BPFP=1.6561 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,988,872B, BPFP=1.8972 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,983,432B, BPFP=1.8937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,856,192B, BPFP=1.1782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,879,936B, BPFP=1.1933 +⌛️ [2/4] FRONTEND: Frontend time: 33.119s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.772s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14577075 0.36048573 + layer.9.1 0.14541274 4.31925691 + layer.19.0 0.13069581 10.86197367 + layer.19.1 0.13545482 4.93570842 + layer.29.0 0.11331055 25.51191044 + layer.29.1 0.11244963 25.85803441 + layer.39.0 32.27446072 2274.39876503 + layer.39.1 16.59366367 2145.12089698 + ------------------------------------------------------------------------------------- + TOTAL 6.20640234 561.42087895 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 18767720 +BPFP 1.4891 bits/point +EBPFP 1.4891 equivalent bits/point +MSE 561.420879 +---------------------- ---------------------------------------------------------- +Time: 66.046s Load: 1.154s, Pack+Encode: 33.119s, Decode+Unpack: 31.772s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 561.4209 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00327-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00327-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.231s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,680,056B, BPFP=1.0664 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,680,136B, BPFP=1.0665 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,435,700B, BPFP=1.5461 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,426,012B, BPFP=1.5399 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,779,800B, BPFP=1.7645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,792,660B, BPFP=1.7726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,656,936B, BPFP=1.0517 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,672,852B, BPFP=1.0618 +⌛️ [2/4] FRONTEND: Frontend time: 33.034s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.692s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585920 0.47453332 + layer.9.1 0.14576220 0.34029930 + layer.19.0 0.12270736 6.37506792 + layer.19.1 0.12453605 7.35334551 + layer.29.0 0.11393550 33.06784967 + layer.29.1 0.11678154 33.97937317 + layer.39.0 53.83016636 1425.29525512 + layer.39.1 40.65720720 1729.51039974 + ------------------------------------------------------------------------------------- + TOTAL 11.90711942 404.54951547 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17124152 +BPFP 1.3587 bits/point +EBPFP 1.3587 equivalent bits/point +MSE 404.549515 +---------------------- ---------------------------------------------------------- +Time: 65.957s Load: 1.231s, Pack+Encode: 33.034s, Decode+Unpack: 31.692s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 404.5495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/classroom-rgb_00328-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/classroom-rgb_00328-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,641,468B, BPFP=1.0419 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,630,692B, BPFP=1.0351 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,473,300B, BPFP=1.5699 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,488,608B, BPFP=1.5796 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,874,596B, BPFP=1.8246 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,874,048B, BPFP=1.8243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,600,356B, BPFP=1.0158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,599,188B, BPFP=1.0151 +⌛️ [2/4] FRONTEND: Frontend time: 32.949s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14568599 4.29377330 + layer.9.1 0.03329684 4.34181719 + layer.19.0 0.11848472 6.81938132 + layer.19.1 0.11973745 2.24295555 + layer.29.0 0.10886538 36.02546362 + layer.29.1 0.10946879 34.86072829 + layer.39.0 14.08931437 1124.25203120 + layer.39.1 9.95616799 1203.06061098 + ------------------------------------------------------------------------------------- + TOTAL 3.08512769 301.98709518 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17182256 +BPFP 1.3633 bits/point +EBPFP 1.3633 equivalent bits/point +MSE 301.987095 +---------------------- ---------------------------------------------------------- +Time: 66.166s Load: 1.169s, Pack+Encode: 32.949s, Decode+Unpack: 32.048s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 301.9871 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00332-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00332-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.190s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,706,288B, BPFP=1.0831 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,726,292B, BPFP=1.0958 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,435,860B, BPFP=1.5462 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,453,768B, BPFP=1.5575 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,776,032B, BPFP=1.7621 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,792,340B, BPFP=1.7724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,631,912B, BPFP=1.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,613,300B, BPFP=1.0240 +⌛️ [2/4] FRONTEND: Frontend time: 33.216s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.771s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14493624 4.28317044 + layer.9.1 0.14482686 4.27940700 + layer.19.0 0.11946148 5.51963218 + layer.19.1 0.12828579 7.14870981 + layer.29.0 0.10467725 28.88965002 + layer.29.1 0.10613328 31.92458665 + layer.39.0 22.00188902 1610.47627559 + layer.39.1 19.26198661 1351.31312967 + ------------------------------------------------------------------------------------- + TOTAL 5.25152457 380.47932017 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17135792 +BPFP 1.3596 bits/point +EBPFP 1.3596 equivalent bits/point +MSE 380.479320 +---------------------- ---------------------------------------------------------- +Time: 66.177s Load: 1.190s, Pack+Encode: 33.216s, Decode+Unpack: 31.771s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 380.4793 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00333-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00333-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.199s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,747,948B, BPFP=1.1095 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,730,932B, BPFP=1.0987 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,471,104B, BPFP=1.5685 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,443,284B, BPFP=1.5509 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,757,564B, BPFP=1.7504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,709,652B, BPFP=1.7200 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,601,408B, BPFP=1.0165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,587,832B, BPFP=1.0079 +⌛️ [2/4] FRONTEND: Frontend time: 33.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.623s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14475438 2.96042843 + layer.9.1 0.14492096 0.46033889 + layer.19.0 0.11744098 5.61334092 + layer.19.1 0.11578254 7.40469550 + layer.29.0 0.11402616 28.58430746 + layer.29.1 0.11062706 28.73379103 + layer.39.0 28.92800668 1635.14592135 + layer.39.1 10.80449708 1699.93776406 + ------------------------------------------------------------------------------------- + TOTAL 5.06000698 426.10507345 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17049724 +BPFP 1.3528 bits/point +EBPFP 1.3528 equivalent bits/point +MSE 426.105073 +---------------------- ---------------------------------------------------------- +Time: 66.434s Load: 1.199s, Pack+Encode: 33.612s, Decode+Unpack: 31.623s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 426.1051 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/computer_lab-rgb_00334-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/computer_lab-rgb_00334-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,601,832B, BPFP=1.0168 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,597,792B, BPFP=1.0142 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,323,120B, BPFP=1.4746 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,325,668B, BPFP=1.4762 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,490,284B, BPFP=1.5807 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,479,024B, BPFP=1.5736 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,304,192B, BPFP=0.8278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,356,324B, BPFP=0.8609 +⌛️ [2/4] FRONTEND: Frontend time: 33.276s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.715s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14546214 4.36131575 + layer.9.1 0.14553630 8.25834506 + layer.19.0 0.04765745 4.77176081 + layer.19.1 0.04191649 6.74002046 + layer.29.0 0.16505912 37.84357989 + layer.29.1 0.15755973 44.63935550 + layer.39.0 42.51041751 732.18882028 + layer.39.1 31.38856333 950.45693858 + ------------------------------------------------------------------------------------- + TOTAL 9.32527151 223.65751704 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15478236 +BPFP 1.2281 bits/point +EBPFP 1.2281 equivalent bits/point +MSE 223.657517 +---------------------- ---------------------------------------------------------- +Time: 66.172s Load: 1.181s, Pack+Encode: 33.276s, Decode+Unpack: 31.715s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 223.6575 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01347-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01347-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.193s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,624,736B, BPFP=1.0313 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,603,236B, BPFP=1.0177 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,414,580B, BPFP=1.5327 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,448,180B, BPFP=1.5540 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,725,156B, BPFP=1.7298 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,725,008B, BPFP=1.7297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,508,180B, BPFP=0.9573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,495,020B, BPFP=0.9490 +⌛️ [2/4] FRONTEND: Frontend time: 32.975s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03343166 0.34768569 + layer.9.1 0.03311388 0.31814977 + layer.19.0 0.03842411 2.40147240 + layer.19.1 0.03806642 7.07646069 + layer.29.0 4.26870163 41.10038694 + layer.29.1 4.26552788 44.67729830 + layer.39.0 33.95300821 897.31247969 + layer.39.1 48.19954501 950.69995125 + ------------------------------------------------------------------------------------- + TOTAL 11.35372735 242.99173559 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16544096 +BPFP 1.3127 bits/point +EBPFP 1.3127 equivalent bits/point +MSE 242.991736 +---------------------- ---------------------------------------------------------- +Time: 65.695s Load: 1.193s, Pack+Encode: 32.975s, Decode+Unpack: 31.527s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 242.9917 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01390-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01390-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.206s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,674,212B, BPFP=1.0627 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,683,684B, BPFP=1.0687 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,446,780B, BPFP=1.5531 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,483,800B, BPFP=1.5766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,706,080B, BPFP=1.7177 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,693,032B, BPFP=1.7094 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,490,344B, BPFP=0.9460 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,507,972B, BPFP=0.9572 +⌛️ [2/4] FRONTEND: Frontend time: 33.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.396s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549638 4.36730207 + layer.9.1 0.14520178 3.03301936 + layer.19.0 0.11487435 8.32547619 + layer.19.1 0.11481158 8.71721391 + layer.29.0 0.10827909 45.06486635 + layer.29.1 0.10618535 45.63928949 + layer.39.0 9.83978281 1084.27356191 + layer.39.1 9.67554703 1149.47733182 + ------------------------------------------------------------------------------------- + TOTAL 2.53127230 293.61225764 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16685904 +BPFP 1.3239 bits/point +EBPFP 1.3239 equivalent bits/point +MSE 293.612258 +---------------------- ---------------------------------------------------------- +Time: 66.232s Load: 1.206s, Pack+Encode: 33.629s, Decode+Unpack: 31.396s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 293.6123 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01413-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.064s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,646,216B, BPFP=1.0449 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,676,668B, BPFP=1.0643 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,426,264B, BPFP=1.5401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,431,052B, BPFP=1.5431 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,736,356B, BPFP=1.7369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,778,748B, BPFP=1.7638 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,476,476B, BPFP=0.9372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,440,740B, BPFP=0.9145 +⌛️ [2/4] FRONTEND: Frontend time: 33.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.958s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092318 3.07479129 + layer.9.1 0.00095285 0.50296761 + layer.19.0 0.08568402 13.07572945 + layer.19.1 0.08404610 4.22236427 + layer.29.0 0.12100375 41.56671982 + layer.29.1 0.12795564 43.53760765 + layer.39.0 12.85620633 1401.96490088 + layer.39.1 12.98640239 1194.08401040 + ------------------------------------------------------------------------------------- + TOTAL 3.28289678 337.75363642 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16612520 +BPFP 1.3181 bits/point +EBPFP 1.3181 equivalent bits/point +MSE 337.753636 +---------------------- ---------------------------------------------------------- +Time: 66.674s Load: 1.064s, Pack+Encode: 33.653s, Decode+Unpack: 31.958s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 337.7536 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01421-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01421-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.230s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,634,808B, BPFP=1.0377 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,653,056B, BPFP=1.0493 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,427,792B, BPFP=1.5410 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,398,856B, BPFP=1.5227 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,792,648B, BPFP=1.7726 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,779,012B, BPFP=1.7640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,582,364B, BPFP=1.0044 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,606,036B, BPFP=1.0194 +⌛️ [2/4] FRONTEND: Frontend time: 33.759s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.946s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00113376 0.45582066 + layer.9.1 0.00100095 3.03952777 + layer.19.0 0.00983371 7.71685591 + layer.19.1 0.00806405 12.53795169 + layer.29.0 4.28365570 38.69928603 + layer.29.1 4.28597952 41.19107999 + layer.39.0 8.41906814 1137.43670783 + layer.39.1 8.59662605 1633.80305492 + ------------------------------------------------------------------------------------- + TOTAL 3.20067024 359.36003560 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16874572 +BPFP 1.3389 bits/point +EBPFP 1.3389 equivalent bits/point +MSE 359.360036 +---------------------- ---------------------------------------------------------- +Time: 66.935s Load: 1.230s, Pack+Encode: 33.759s, Decode+Unpack: 31.946s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 359.3600 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01422-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01422-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.185s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,757,952B, BPFP=1.1159 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,724,504B, BPFP=1.0946 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,465,716B, BPFP=1.5651 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,460,100B, BPFP=1.5615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,864,968B, BPFP=1.8185 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,841,888B, BPFP=1.8039 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,658,528B, BPFP=1.0528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,622,508B, BPFP=1.0299 +⌛️ [2/4] FRONTEND: Frontend time: 33.375s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.778s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550294 3.02722156 + layer.9.1 0.14526658 0.34941601 + layer.19.0 0.11599200 3.19804732 + layer.19.1 0.11361485 3.30578784 + layer.29.0 4.26439454 38.80896064 + layer.29.1 4.25587461 40.00819843 + layer.39.0 8.37236706 1576.39372766 + layer.39.1 8.35116642 1389.17907052 + ------------------------------------------------------------------------------------- + TOTAL 3.22052237 381.78380375 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17396164 +BPFP 1.3803 bits/point +EBPFP 1.3803 equivalent bits/point +MSE 381.783804 +---------------------- ---------------------------------------------------------- +Time: 66.338s Load: 1.185s, Pack+Encode: 33.375s, Decode+Unpack: 31.778s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 381.7838 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/dining_room-rgb_01441-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/dining_room-rgb_01441-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.164s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,656,836B, BPFP=1.0517 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,649,640B, BPFP=1.0471 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,507,768B, BPFP=1.5918 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,533,424B, BPFP=1.6081 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,864,180B, BPFP=1.8180 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,866,368B, BPFP=1.8194 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,615,660B, BPFP=1.0255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,644,196B, BPFP=1.0437 +⌛️ [2/4] FRONTEND: Frontend time: 33.819s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00080404 0.35784577 + layer.9.1 0.00082438 0.36081287 + layer.19.0 0.00843097 6.19311309 + layer.19.1 0.00674472 6.58949718 + layer.29.0 4.27713270 43.83604668 + layer.29.1 4.27133426 41.21681020 + layer.39.0 22.97048921 1383.86366591 + layer.39.1 18.06488920 1406.63210920 + ------------------------------------------------------------------------------------- + TOTAL 6.20008118 361.13123761 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17338072 +BPFP 1.3757 bits/point +EBPFP 1.3757 equivalent bits/point +MSE 361.131238 +---------------------- ---------------------------------------------------------- +Time: 67.009s Load: 1.164s, Pack+Encode: 33.819s, Decode+Unpack: 32.027s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 361.1312 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00350-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00350-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,606,300B, BPFP=1.0196 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,655,008B, BPFP=1.0505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,466,268B, BPFP=1.5655 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,475,968B, BPFP=1.5716 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,840,064B, BPFP=1.8027 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,852,912B, BPFP=1.8109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,609,384B, BPFP=1.0216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,615,552B, BPFP=1.0255 +⌛️ [2/4] FRONTEND: Frontend time: 33.407s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.806s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14506060 0.70220874 + layer.9.1 0.14523201 3.00011203 + layer.19.0 0.04621643 6.88246910 + layer.19.1 0.04629335 5.62854444 + layer.29.0 4.27940669 40.08940831 + layer.29.1 4.27759670 39.67326637 + layer.39.0 19.91382637 1432.14202145 + layer.39.1 24.01088215 1367.16948326 + ------------------------------------------------------------------------------------- + TOTAL 6.60806429 361.91093921 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17121456 +BPFP 1.3585 bits/point +EBPFP 1.3585 equivalent bits/point +MSE 361.910939 +---------------------- ---------------------------------------------------------- +Time: 66.385s Load: 1.172s, Pack+Encode: 33.407s, Decode+Unpack: 31.806s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 361.9109 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/foyer-rgb_00351-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/foyer-rgb_00351-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.214s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,461,128B, BPFP=0.9275 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,490,888B, BPFP=0.9463 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,143,152B, BPFP=1.3604 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,181,492B, BPFP=1.3847 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,281,828B, BPFP=1.4484 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,325,576B, BPFP=1.4762 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,189,952B, BPFP=0.7553 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,192,392B, BPFP=0.7569 +⌛️ [2/4] FRONTEND: Frontend time: 33.379s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.671s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66985745 0.45900294 + layer.9.1 2.66884121 4.39185483 + layer.19.0 3.21935619 4.55180637 + layer.19.1 3.21606501 12.57661683 + layer.29.0 4.24164606 41.65525217 + layer.29.1 4.23648681 44.28846076 + layer.39.0 8.06392628 565.38726844 + layer.39.1 8.17747540 593.55557361 + ------------------------------------------------------------------------------------- + TOTAL 4.56170680 158.35822949 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14266408 +BPFP 1.1319 bits/point +EBPFP 1.1319 equivalent bits/point +MSE 158.358229 +---------------------- ---------------------------------------------------------- +Time: 66.264s Load: 1.214s, Pack+Encode: 33.379s, Decode+Unpack: 31.671s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 158.3582 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00358-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00358-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.191s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,451,548B, BPFP=0.9214 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,455,408B, BPFP=0.9238 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,144,548B, BPFP=1.3613 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,178,704B, BPFP=1.3829 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,405,208B, BPFP=1.5267 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,431,960B, BPFP=1.5437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,375,308B, BPFP=0.8730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,357,968B, BPFP=0.8620 +⌛️ [2/4] FRONTEND: Frontend time: 33.035s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.900s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66795352 0.33174315 + layer.9.1 2.66862889 3.03773397 + layer.19.0 3.22250645 13.51015854 + layer.19.1 3.22577319 8.15451332 + layer.29.0 4.25792136 39.70824108 + layer.29.1 4.25014663 42.89109725 + layer.39.0 8.65209937 1197.73578161 + layer.39.1 8.58450170 1177.91915827 + ------------------------------------------------------------------------------------- + TOTAL 4.69119139 310.41105340 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14800652 +BPFP 1.1743 bits/point +EBPFP 1.1743 equivalent bits/point +MSE 310.411053 +---------------------- ---------------------------------------------------------- +Time: 66.126s Load: 1.191s, Pack+Encode: 33.035s, Decode+Unpack: 31.900s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 310.4111 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00360-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00360-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,687,972B, BPFP=1.0714 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,682,112B, BPFP=1.0677 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,491,640B, BPFP=1.5816 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,500,928B, BPFP=1.5875 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,889,728B, BPFP=1.8343 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,908,624B, BPFP=1.8462 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,567,968B, BPFP=0.9953 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,538,508B, BPFP=0.9766 +⌛️ [2/4] FRONTEND: Frontend time: 33.762s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.104s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00092775 0.35367491 + layer.9.1 0.00093166 0.34559058 + layer.19.0 0.08227225 5.39709183 + layer.19.1 0.08381199 6.74542536 + layer.29.0 0.10725604 32.83422368 + layer.29.1 0.10756977 32.21152655 + layer.39.0 7.96294394 1530.92102697 + layer.39.1 7.95922050 963.17679558 + ------------------------------------------------------------------------------------- + TOTAL 2.03811674 321.49816943 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17267480 +BPFP 1.3701 bits/point +EBPFP 1.3701 equivalent bits/point +MSE 321.498169 +---------------------- ---------------------------------------------------------- +Time: 67.075s Load: 1.209s, Pack+Encode: 33.762s, Decode+Unpack: 32.104s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 321.4982 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00361-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00361-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.093s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,580,568B, BPFP=1.0033 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,605,456B, BPFP=1.0191 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,325,720B, BPFP=1.4763 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,368,796B, BPFP=1.5036 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,463,256B, BPFP=1.5636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,498,932B, BPFP=1.5862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,255,756B, BPFP=0.7971 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,252,816B, BPFP=0.7952 +⌛️ [2/4] FRONTEND: Frontend time: 33.422s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.403s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66308118 0.41765669 + layer.9.1 2.66351027 0.42017407 + layer.19.0 3.21594155 9.87836037 + layer.19.1 3.21498593 9.12555731 + layer.29.0 4.33566519 51.10074748 + layer.29.1 4.34101296 53.80084092 + layer.39.0 8.65310735 662.46083848 + layer.39.1 8.66575030 652.04094898 + ------------------------------------------------------------------------------------- + TOTAL 4.71913184 179.90564054 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15351300 +BPFP 1.2180 bits/point +EBPFP 1.2180 equivalent bits/point +MSE 179.905641 +---------------------- ---------------------------------------------------------- +Time: 65.917s Load: 1.093s, Pack+Encode: 33.422s, Decode+Unpack: 31.403s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 179.9056 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00384-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00384-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.019s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,530,420B, BPFP=0.9714 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,543,584B, BPFP=0.9798 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,286,596B, BPFP=1.4514 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,317,156B, BPFP=1.4708 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,459,460B, BPFP=1.5611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,478,948B, BPFP=1.5735 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,198,548B, BPFP=0.7608 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,194,268B, BPFP=0.7581 +⌛️ [2/4] FRONTEND: Frontend time: 33.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.956s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.65959259 3.09920663 + layer.9.1 2.65993726 0.34926609 + layer.19.0 3.20866700 7.75006221 + layer.19.1 3.21007805 12.66531804 + layer.29.0 4.27255361 45.53184413 + layer.29.1 4.27602442 44.80355765 + layer.39.0 19.11658068 990.48951901 + layer.39.1 9.60360322 888.40396490 + ------------------------------------------------------------------------------------- + TOTAL 6.12587960 249.13659233 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15008980 +BPFP 1.1909 bits/point +EBPFP 1.1909 equivalent bits/point +MSE 249.136592 +---------------------- ---------------------------------------------------------- +Time: 66.261s Load: 1.019s, Pack+Encode: 33.286s, Decode+Unpack: 31.956s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 249.1366 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00385-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00385-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,596,080B, BPFP=1.0131 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,580,608B, BPFP=1.0033 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,436,432B, BPFP=1.5465 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,420,764B, BPFP=1.5366 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,620,292B, BPFP=1.6632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,620,696B, BPFP=1.6635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,355,556B, BPFP=0.8604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,341,684B, BPFP=0.8516 +⌛️ [2/4] FRONTEND: Frontend time: 33.219s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67078767 4.28455070 + layer.9.1 2.67131261 0.33212648 + layer.19.0 3.30595795 8.21580222 + layer.19.1 3.30543206 3.54621550 + layer.29.0 0.11228124 46.58873294 + layer.29.1 0.11507649 47.05227291 + layer.39.0 11.41791162 896.40591485 + layer.39.1 11.38150745 990.52616185 + ------------------------------------------------------------------------------------- + TOTAL 4.37253339 249.61897218 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15972112 +BPFP 1.2673 bits/point +EBPFP 1.2673 equivalent bits/point +MSE 249.618972 +---------------------- ---------------------------------------------------------- +Time: 65.733s Load: 1.071s, Pack+Encode: 33.219s, Decode+Unpack: 31.443s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 249.6190 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/home_office-rgb_00557-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/home_office-rgb_00557-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.956s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,635,780B, BPFP=1.0383 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,669,772B, BPFP=1.0599 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,396,344B, BPFP=1.5211 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,381,636B, BPFP=1.5117 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,689,360B, BPFP=1.7071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,666,512B, BPFP=1.6926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,458,336B, BPFP=0.9257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,434,940B, BPFP=0.9108 +⌛️ [2/4] FRONTEND: Frontend time: 33.707s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.863s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14463971 8.27358286 + layer.9.1 0.14470460 0.47087030 + layer.19.0 0.12255537 15.39447031 + layer.19.1 0.11825690 3.24945348 + layer.29.0 0.11949990 38.69324068 + layer.29.1 0.11467140 42.05803644 + layer.39.0 10.68243977 1107.70206370 + layer.39.1 10.40156301 970.45035749 + ------------------------------------------------------------------------------------- + TOTAL 2.73104133 273.28650941 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16332680 +BPFP 1.2959 bits/point +EBPFP 1.2959 equivalent bits/point +MSE 273.286509 +---------------------- ---------------------------------------------------------- +Time: 66.525s Load: 0.956s, Pack+Encode: 33.707s, Decode+Unpack: 31.863s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 273.2865 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00001-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00001-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.229s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,725,600B, BPFP=1.0953 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,727,432B, BPFP=1.0965 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,471,504B, BPFP=1.5688 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,492,924B, BPFP=1.5824 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,745,588B, BPFP=1.7428 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,751,432B, BPFP=1.7465 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,517,644B, BPFP=0.9633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,484,504B, BPFP=0.9423 +⌛️ [2/4] FRONTEND: Frontend time: 33.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14485273 0.35516772 + layer.9.1 0.14484227 0.35615364 + layer.19.0 0.11969613 3.02548235 + layer.19.1 0.11916645 3.49536379 + layer.29.0 0.11480527 39.47882983 + layer.29.1 0.11451660 38.31095883 + layer.39.0 11.00270276 1429.90428989 + layer.39.1 11.01557422 1033.08401040 + ------------------------------------------------------------------------------------- + TOTAL 2.84701956 318.50128206 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16916628 +BPFP 1.3422 bits/point +EBPFP 1.3422 equivalent bits/point +MSE 318.501282 +---------------------- ---------------------------------------------------------- +Time: 66.823s Load: 1.229s, Pack+Encode: 33.529s, Decode+Unpack: 32.065s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 318.5013 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00126-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00126-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.231s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,618,768B, BPFP=1.0275 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,640,268B, BPFP=1.0412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,307,024B, BPFP=1.4644 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,312,996B, BPFP=1.4682 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,415,592B, BPFP=1.5333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,394,164B, BPFP=1.5197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,415,024B, BPFP=0.8982 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,394,204B, BPFP=0.8850 +⌛️ [2/4] FRONTEND: Frontend time: 33.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.657s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14481488 3.07544477 + layer.9.1 0.14470567 0.31994794 + layer.19.0 0.03819180 8.38424576 + layer.19.1 0.04002141 9.57171085 + layer.29.0 0.11241068 35.29111909 + layer.29.1 0.11133552 31.72172317 + layer.39.0 31.78807483 1203.95490738 + layer.39.1 43.50691623 1287.76949951 + ------------------------------------------------------------------------------------- + TOTAL 9.48580888 322.51107481 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15498040 +BPFP 1.2297 bits/point +EBPFP 1.2297 equivalent bits/point +MSE 322.511075 +---------------------- ---------------------------------------------------------- +Time: 66.419s Load: 1.231s, Pack+Encode: 33.530s, Decode+Unpack: 31.657s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 322.5111 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00127-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00127-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,626,956B, BPFP=1.0327 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,621,216B, BPFP=1.0291 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,376,380B, BPFP=1.5084 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,353,920B, BPFP=1.4942 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,663,836B, BPFP=1.6909 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,625,292B, BPFP=1.6664 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,389,172B, BPFP=0.8818 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,394,700B, BPFP=0.8853 +⌛️ [2/4] FRONTEND: Frontend time: 33.440s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14528272 4.39791574 + layer.9.1 0.14516892 8.23362536 + layer.19.0 0.11319376 7.97320213 + layer.19.1 0.11666145 3.50040656 + layer.29.0 0.21118872 39.39288979 + layer.29.1 0.20646930 39.80854678 + layer.39.0 14.37750853 1352.11147221 + layer.39.1 21.76644002 1145.58271043 + ------------------------------------------------------------------------------------- + TOTAL 4.63523918 325.12509612 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16051472 +BPFP 1.2736 bits/point +EBPFP 1.2736 equivalent bits/point +MSE 325.125096 +---------------------- ---------------------------------------------------------- +Time: 66.727s Load: 1.228s, Pack+Encode: 33.440s, Decode+Unpack: 32.058s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 325.1251 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00128-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00128-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.182s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,609,848B, BPFP=1.0219 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,615,048B, BPFP=1.0252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,392,676B, BPFP=1.5188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,418,504B, BPFP=1.5351 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,605,172B, BPFP=1.6536 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,609,940B, BPFP=1.6567 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,453,692B, BPFP=0.9227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,466,884B, BPFP=0.9311 +⌛️ [2/4] FRONTEND: Frontend time: 33.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.152s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14454765 3.05140330 + layer.9.1 0.14475082 0.37352238 + layer.19.0 0.04087094 8.13484433 + layer.19.1 0.11687931 6.88484814 + layer.29.0 0.10817139 43.20882759 + layer.29.1 0.10802081 42.19096827 + layer.39.0 19.80422286 871.90802730 + layer.39.1 34.29222355 1454.78680533 + ------------------------------------------------------------------------------------- + TOTAL 6.84496092 303.81740583 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16171764 +BPFP 1.2831 bits/point +EBPFP 1.2831 equivalent bits/point +MSE 303.817406 +---------------------- ---------------------------------------------------------- +Time: 66.480s Load: 1.182s, Pack+Encode: 33.146s, Decode+Unpack: 32.152s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 303.8174 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00131-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00131-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.228s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,553,272B, BPFP=0.9859 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,592,260B, BPFP=1.0107 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,342,548B, BPFP=1.4869 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,363,748B, BPFP=1.5004 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,532,200B, BPFP=1.6073 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,568,156B, BPFP=1.6301 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,351,928B, BPFP=0.8581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,369,476B, BPFP=0.8693 +⌛️ [2/4] FRONTEND: Frontend time: 33.691s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14462205 0.60147062 + layer.9.1 0.14495783 4.29698005 + layer.19.0 0.04322015 9.33823529 + layer.19.1 0.03788725 7.06927595 + layer.29.0 0.10021623 44.48955456 + layer.29.1 0.10137775 41.05412638 + layer.39.0 58.66958482 776.69223269 + layer.39.1 72.48303949 871.49520637 + ------------------------------------------------------------------------------------- + TOTAL 16.46561320 219.37963524 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15673588 +BPFP 1.2436 bits/point +EBPFP 1.2436 equivalent bits/point +MSE 219.379635 +---------------------- ---------------------------------------------------------- +Time: 66.969s Load: 1.228s, Pack+Encode: 33.691s, Decode+Unpack: 32.050s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 219.3796 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00132-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00132-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.217s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,722,360B, BPFP=1.0933 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,735,080B, BPFP=1.1013 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,507,052B, BPFP=1.5914 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,508,852B, BPFP=1.5925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,770,784B, BPFP=1.7588 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,782,468B, BPFP=1.7662 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,435,100B, BPFP=0.9109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,472,148B, BPFP=0.9344 +⌛️ [2/4] FRONTEND: Frontend time: 33.708s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.109s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14527927 4.19685113 + layer.9.1 0.14528875 2.98686734 + layer.19.0 0.12591341 2.03560232 + layer.19.1 0.13556211 6.85753676 + layer.29.0 0.11238900 34.09001259 + layer.29.1 0.11028371 35.14700550 + layer.39.0 11.48751193 1196.75146246 + layer.39.1 11.29491489 1396.82759181 + ------------------------------------------------------------------------------------- + TOTAL 2.94464289 334.86161624 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16933844 +BPFP 1.3436 bits/point +EBPFP 1.3436 equivalent bits/point +MSE 334.861616 +---------------------- ---------------------------------------------------------- +Time: 67.034s Load: 1.217s, Pack+Encode: 33.708s, Decode+Unpack: 32.109s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 334.8616 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00136-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00136-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,673,416B, BPFP=1.0622 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,687,484B, BPFP=1.0711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,427,132B, BPFP=1.5406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,450,760B, BPFP=1.5556 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,708,968B, BPFP=1.7195 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,716,284B, BPFP=1.7242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,440,392B, BPFP=0.9143 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,434,596B, BPFP=0.9106 +⌛️ [2/4] FRONTEND: Frontend time: 33.776s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.146s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14489184 0.31608439 + layer.9.1 0.14511764 3.03713857 + layer.19.0 0.03976490 7.38992233 + layer.19.1 0.11370806 3.08079000 + layer.29.0 0.10933599 45.90859604 + layer.29.1 0.11012027 49.58167452 + layer.39.0 9.10787636 861.75901852 + layer.39.1 9.00026152 848.46741956 + ------------------------------------------------------------------------------------- + TOTAL 2.34638457 227.44258049 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16539032 +BPFP 1.3123 bits/point +EBPFP 1.3123 equivalent bits/point +MSE 227.442580 +---------------------- ---------------------------------------------------------- +Time: 67.100s Load: 1.179s, Pack+Encode: 33.776s, Decode+Unpack: 32.146s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 227.4426 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00193-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00193-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.216s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,607,108B, BPFP=1.0201 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,621,464B, BPFP=1.0292 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,378,856B, BPFP=1.5100 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,387,380B, BPFP=1.5154 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,505,652B, BPFP=1.5905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,509,180B, BPFP=1.5927 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,114,500B, BPFP=0.7074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,090,400B, BPFP=0.6921 +⌛️ [2/4] FRONTEND: Frontend time: 33.255s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083307 4.29407924 + layer.9.1 0.00247171 0.34957236 + layer.19.0 0.00642632 13.44192674 + layer.19.1 0.00641681 9.34581357 + layer.29.0 0.10256791 55.41265336 + layer.29.1 0.10162673 54.79490981 + layer.39.0 8.50517638 718.20425739 + layer.39.1 8.55767781 643.94783880 + ------------------------------------------------------------------------------------- + TOTAL 2.16039959 187.47388141 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15214540 +BPFP 1.2072 bits/point +EBPFP 1.2072 equivalent bits/point +MSE 187.473881 +---------------------- ---------------------------------------------------------- +Time: 66.081s Load: 1.216s, Pack+Encode: 33.255s, Decode+Unpack: 31.610s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 187.4739 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00196-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00196-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.075s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,605,612B, BPFP=1.0192 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,612,892B, BPFP=1.0238 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,383,812B, BPFP=1.5131 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,373,408B, BPFP=1.5065 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,692,656B, BPFP=1.7092 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,660,136B, BPFP=1.6885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,406,888B, BPFP=0.8930 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,404,284B, BPFP=0.8914 +⌛️ [2/4] FRONTEND: Frontend time: 32.987s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00064391 0.33893296 + layer.9.1 0.00065402 0.44168538 + layer.19.0 0.08134466 8.05689199 + layer.19.1 0.08141702 7.53134077 + layer.29.0 0.11551180 45.20611086 + layer.29.1 0.11251285 42.89291010 + layer.39.0 10.61319619 998.56767956 + layer.39.1 10.43102047 980.49666883 + ------------------------------------------------------------------------------------- + TOTAL 2.67953762 260.44152756 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16139688 +BPFP 1.2806 bits/point +EBPFP 1.2806 equivalent bits/point +MSE 260.441528 +---------------------- ---------------------------------------------------------- +Time: 65.668s Load: 1.075s, Pack+Encode: 32.987s, Decode+Unpack: 31.606s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 260.4415 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00559-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00559-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,615,800B, BPFP=1.0256 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,643,564B, BPFP=1.0433 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,383,896B, BPFP=1.5132 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,435,400B, BPFP=1.5459 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,749,084B, BPFP=1.7450 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,751,824B, BPFP=1.7467 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,560,664B, BPFP=0.9906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,597,632B, BPFP=1.0141 +⌛️ [2/4] FRONTEND: Frontend time: 33.267s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.864s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14441776 0.49364719 + layer.9.1 0.14449203 2.98120665 + layer.19.0 0.11315974 8.69743891 + layer.19.1 0.11435745 8.24603408 + layer.29.0 0.12811458 39.13687490 + layer.29.1 0.12952277 41.88752488 + layer.39.0 31.10682331 1168.01527462 + layer.39.1 16.99297713 1296.04549886 + ------------------------------------------------------------------------------------- + TOTAL 6.10923309 320.68793751 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16737864 +BPFP 1.3280 bits/point +EBPFP 1.3280 equivalent bits/point +MSE 320.687938 +---------------------- ---------------------------------------------------------- +Time: 66.312s Load: 1.181s, Pack+Encode: 33.267s, Decode+Unpack: 31.864s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 320.6879 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00570-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00570-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.188s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,617,720B, BPFP=1.0268 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,611,872B, BPFP=1.0231 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,414,564B, BPFP=1.5326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,425,768B, BPFP=1.5398 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,693,892B, BPFP=1.7099 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,706,392B, BPFP=1.7179 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,407,212B, BPFP=0.8932 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,410,028B, BPFP=0.8950 +⌛️ [2/4] FRONTEND: Frontend time: 33.106s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.347s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078516 4.37207190 + layer.9.1 0.00079184 0.46445437 + layer.19.0 3.22632161 3.11974522 + layer.19.1 3.22513146 6.91087796 + layer.29.0 0.10494786 41.36313272 + layer.29.1 0.10251782 48.59726397 + layer.39.0 10.88842496 897.56280468 + layer.39.1 10.78217420 924.70149496 + ------------------------------------------------------------------------------------- + TOTAL 3.54138686 240.88648072 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16287448 +BPFP 1.2923 bits/point +EBPFP 1.2923 equivalent bits/point +MSE 240.886481 +---------------------- ---------------------------------------------------------- +Time: 65.641s Load: 1.188s, Pack+Encode: 33.106s, Decode+Unpack: 31.347s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 240.8865 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00758-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00758-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.138s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,499,108B, BPFP=0.9516 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,497,832B, BPFP=0.9507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,207,080B, BPFP=1.4009 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,192,300B, BPFP=1.3916 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,448,272B, BPFP=1.5540 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,473,424B, BPFP=1.5700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,284,928B, BPFP=0.8156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,284,588B, BPFP=0.8154 +⌛️ [2/4] FRONTEND: Frontend time: 32.970s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14561636 0.31045835 + layer.9.1 0.14552785 4.32291180 + layer.19.0 0.04069186 8.65943390 + layer.19.1 0.03840616 13.85638914 + layer.29.0 0.11346353 41.11448601 + layer.29.1 0.11182956 42.92503351 + layer.39.0 10.19697364 799.77250569 + layer.39.1 10.11578978 756.06215470 + ------------------------------------------------------------------------------------- + TOTAL 2.61353734 208.37792164 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14887532 +BPFP 1.1812 bits/point +EBPFP 1.1812 equivalent bits/point +MSE 208.377922 +---------------------- ---------------------------------------------------------- +Time: 66.103s Load: 1.138s, Pack+Encode: 32.970s, Decode+Unpack: 31.995s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 208.3779 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00764-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00764-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.232s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,629,100B, BPFP=1.0341 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,623,436B, BPFP=1.0305 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,410,696B, BPFP=1.5302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,430,676B, BPFP=1.5429 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,726,344B, BPFP=1.7305 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,722,304B, BPFP=1.7280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,444,308B, BPFP=0.9168 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,415,544B, BPFP=0.8985 +⌛️ [2/4] FRONTEND: Frontend time: 32.828s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.770s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14399445 4.33571629 + layer.9.1 0.14558028 8.26206025 + layer.19.0 0.03837104 3.30299907 + layer.19.1 0.04376782 7.27011141 + layer.29.0 0.11695251 46.49653173 + layer.29.1 0.13128335 44.80515721 + layer.39.0 11.28613757 1114.96620084 + layer.39.1 11.84408769 957.91826454 + ------------------------------------------------------------------------------------- + TOTAL 2.96877184 273.41963017 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16402408 +BPFP 1.3014 bits/point +EBPFP 1.3014 equivalent bits/point +MSE 273.419630 +---------------------- ---------------------------------------------------------- +Time: 65.830s Load: 1.232s, Pack+Encode: 32.828s, Decode+Unpack: 31.770s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 273.4196 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00845-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00845-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.201s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,606,484B, BPFP=1.0197 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,595,572B, BPFP=1.0128 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,458,120B, BPFP=1.5603 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,473,748B, BPFP=1.5702 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,882,848B, BPFP=1.8299 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,844,788B, BPFP=1.8057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,622,860B, BPFP=1.0301 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,636,448B, BPFP=1.0387 +⌛️ [2/4] FRONTEND: Frontend time: 33.783s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.254s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14502686 0.44522205 + layer.9.1 0.03259508 0.46265593 + layer.19.0 0.11326540 6.30653335 + layer.19.1 0.11324834 6.89314369 + layer.29.0 0.12250664 31.29718984 + layer.29.1 0.12058897 29.83472132 + layer.39.0 16.17915050 1796.39194020 + layer.39.1 21.66230805 1693.61244719 + ------------------------------------------------------------------------------------- + TOTAL 4.81108623 445.65548169 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17120868 +BPFP 1.3584 bits/point +EBPFP 1.3584 equivalent bits/point +MSE 445.655482 +---------------------- ---------------------------------------------------------- +Time: 67.239s Load: 1.201s, Pack+Encode: 33.783s, Decode+Unpack: 32.254s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 445.6555 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/kitchen-rgb_00870-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/kitchen-rgb_00870-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,585,792B, BPFP=1.0066 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,579,328B, BPFP=1.0025 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,343,920B, BPFP=1.4878 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,347,944B, BPFP=1.4904 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,667,728B, BPFP=1.6933 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,673,880B, BPFP=1.6972 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,437,492B, BPFP=0.9124 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,439,440B, BPFP=0.9137 +⌛️ [2/4] FRONTEND: Frontend time: 33.408s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.958s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66733019 3.08860726 + layer.9.1 2.66763138 0.44600588 + layer.19.0 3.22293078 8.70149750 + layer.19.1 3.22376992 8.13202160 + layer.29.0 4.27658332 50.85749106 + layer.29.1 4.27160529 49.50885603 + layer.39.0 7.81683598 1163.32109197 + layer.39.1 9.86231960 944.73789405 + ------------------------------------------------------------------------------------- + TOTAL 4.75112581 278.59918317 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16075524 +BPFP 1.2755 bits/point +EBPFP 1.2755 equivalent bits/point +MSE 278.599183 +---------------------- ---------------------------------------------------------- +Time: 66.587s Load: 1.221s, Pack+Encode: 33.408s, Decode+Unpack: 31.958s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 278.5992 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00153-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00153-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.162s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,569,464B, BPFP=0.9962 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,607,436B, BPFP=1.0203 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,252,436B, BPFP=1.4297 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,320,836B, BPFP=1.4732 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,416,096B, BPFP=1.5336 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,510,388B, BPFP=1.5935 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,267,732B, BPFP=0.8047 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,288,404B, BPFP=0.8178 +⌛️ [2/4] FRONTEND: Frontend time: 33.173s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.937s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14549763 0.34271679 + layer.9.1 0.14520254 4.24423838 + layer.19.0 0.04746155 9.58222993 + layer.19.1 0.04383140 10.02997098 + layer.29.0 4.26247378 43.87784368 + layer.29.1 4.25497898 48.05215104 + layer.39.0 7.94138086 814.62967176 + layer.39.1 7.86439079 856.17273318 + ------------------------------------------------------------------------------------- + TOTAL 3.08815219 223.36644447 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15232792 +BPFP 1.2086 bits/point +EBPFP 1.2086 equivalent bits/point +MSE 223.366444 +---------------------- ---------------------------------------------------------- +Time: 66.272s Load: 1.162s, Pack+Encode: 33.173s, Decode+Unpack: 31.937s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 223.3664 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00167-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00167-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.093s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,498,964B, BPFP=0.9515 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,541,836B, BPFP=0.9787 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,192,860B, BPFP=1.3919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,223,000B, BPFP=1.4110 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,336,616B, BPFP=1.4832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,304,704B, BPFP=1.4629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,221,824B, BPFP=0.7756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,217,408B, BPFP=0.7727 +⌛️ [2/4] FRONTEND: Frontend time: 33.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.758s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11283708 0.32179666 + layer.9.1 0.11300174 4.36292198 + layer.19.0 3.22718329 8.93003026 + layer.19.1 3.22892155 4.89783798 + layer.29.0 4.26448309 40.98558610 + layer.29.1 4.25758082 40.23571051 + layer.39.0 9.82393946 759.15355866 + layer.39.1 9.78394007 711.33100422 + ------------------------------------------------------------------------------------- + TOTAL 4.35148589 196.27730580 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 14537212 +BPFP 1.1534 bits/point +EBPFP 1.1534 equivalent bits/point +MSE 196.277306 +---------------------- ---------------------------------------------------------- +Time: 66.009s Load: 1.093s, Pack+Encode: 33.159s, Decode+Unpack: 31.758s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 196.2773 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_00208-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_00208-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.234s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,779,960B, BPFP=1.1298 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,784,328B, BPFP=1.1326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,460,468B, BPFP=1.5618 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,436,508B, BPFP=1.5466 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,663,668B, BPFP=1.6908 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,628,064B, BPFP=1.6682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,547,324B, BPFP=0.9822 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,530,088B, BPFP=0.9712 +⌛️ [2/4] FRONTEND: Frontend time: 33.702s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14503861 4.33787412 + layer.9.1 0.14483112 4.17148272 + layer.19.0 0.11529889 7.76033500 + layer.19.1 0.11517203 3.26020487 + layer.29.0 0.11961639 31.28448976 + layer.29.1 0.11795276 29.14593151 + layer.39.0 83.84633978 1489.88430289 + layer.39.1 174.87768118 1555.52372441 + ------------------------------------------------------------------------------------- + TOTAL 32.43524135 390.67104316 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16830408 +BPFP 1.3354 bits/point +EBPFP 1.3354 equivalent bits/point +MSE 390.671043 +---------------------- ---------------------------------------------------------- +Time: 66.928s Load: 1.234s, Pack+Encode: 33.702s, Decode+Unpack: 31.992s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 390.6710 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01201-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01201-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.240s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,461,668B, BPFP=0.9278 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,486,100B, BPFP=0.9433 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,251,364B, BPFP=1.4291 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,253,000B, BPFP=1.4301 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,521,604B, BPFP=1.6006 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,514,528B, BPFP=1.5961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,391,944B, BPFP=0.8835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,394,572B, BPFP=0.8852 +⌛️ [2/4] FRONTEND: Frontend time: 33.350s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.81453913 4.28685071 + layer.9.1 0.14528001 4.45946996 + layer.19.0 3.26598681 8.26301300 + layer.19.1 0.04116655 4.01441580 + layer.29.0 4.28557138 45.12385745 + layer.29.1 4.28198282 39.51044036 + layer.39.0 74.89367180 811.28818655 + layer.39.1 42.04871577 1019.68102047 + ------------------------------------------------------------------------------------- + TOTAL 16.47211428 242.07840679 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15274780 +BPFP 1.2120 bits/point +EBPFP 1.2120 equivalent bits/point +MSE 242.078407 +---------------------- ---------------------------------------------------------- +Time: 66.607s Load: 1.240s, Pack+Encode: 33.350s, Decode+Unpack: 32.017s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 242.0784 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01296-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01296-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.190s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,520,880B, BPFP=0.9654 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,535,480B, BPFP=0.9746 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,301,868B, BPFP=1.4611 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,270,792B, BPFP=1.4414 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,559,620B, BPFP=1.6247 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,534,032B, BPFP=1.6085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,303,128B, BPFP=0.8272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,313,716B, BPFP=0.8339 +⌛️ [2/4] FRONTEND: Frontend time: 33.009s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.909s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66710422 3.04722570 + layer.9.1 2.66812426 4.34326061 + layer.19.0 3.22059776 8.03939955 + layer.19.1 3.22546153 5.58349689 + layer.29.0 0.11226317 50.86602718 + layer.29.1 0.11257672 50.34668000 + layer.39.0 59.39237691 670.16737082 + layer.39.1 37.52358222 699.35700357 + ------------------------------------------------------------------------------------- + TOTAL 13.61526085 186.46880804 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15339516 +BPFP 1.2171 bits/point +EBPFP 1.2171 equivalent bits/point +MSE 186.468808 +---------------------- ---------------------------------------------------------- +Time: 66.108s Load: 1.190s, Pack+Encode: 33.009s, Decode+Unpack: 31.909s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 186.4688 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/living_room-rgb_01313-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/living_room-rgb_01313-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.223s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,564,680B, BPFP=0.9932 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,539,304B, BPFP=0.9771 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,351,840B, BPFP=1.4928 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,315,828B, BPFP=1.4700 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,707,720B, BPFP=1.7187 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,679,948B, BPFP=1.7011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,320,612B, BPFP=0.8383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,324,976B, BPFP=0.8410 +⌛️ [2/4] FRONTEND: Frontend time: 33.230s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14519123 3.01709318 + layer.9.1 0.14511500 3.02138821 + layer.19.0 0.03974548 9.41548434 + layer.19.1 0.03981401 7.56846093 + layer.29.0 4.26343511 52.52505484 + layer.29.1 4.25610090 47.37174500 + layer.39.0 7.90972018 782.04777381 + layer.39.1 8.05601540 644.63828404 + ------------------------------------------------------------------------------------- + TOTAL 3.10689216 193.70066054 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15804908 +BPFP 1.2540 bits/point +EBPFP 1.2540 equivalent bits/point +MSE 193.700661 +---------------------- ---------------------------------------------------------- +Time: 66.494s Load: 1.223s, Pack+Encode: 33.230s, Decode+Unpack: 32.042s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 193.7007 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00032-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00032-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.195s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,546,240B, BPFP=0.9815 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,556,876B, BPFP=0.9882 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,360,904B, BPFP=1.4986 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,363,804B, BPFP=1.5004 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,712,600B, BPFP=1.7218 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,715,388B, BPFP=1.7236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,398,924B, BPFP=0.8880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,389,392B, BPFP=0.8819 +⌛️ [2/4] FRONTEND: Frontend time: 33.500s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.907s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14586543 0.39792546 + layer.9.1 0.14572574 4.27786615 + layer.19.0 0.03953905 9.55382741 + layer.19.1 0.03760033 4.99446022 + layer.29.0 0.10448607 42.82144235 + layer.29.1 0.10697372 39.30925516 + layer.39.0 14.19073468 901.11894703 + layer.39.1 8.92149669 867.17127072 + ------------------------------------------------------------------------------------- + TOTAL 2.96155271 233.70562431 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16044128 +BPFP 1.2730 bits/point +EBPFP 1.2730 equivalent bits/point +MSE 233.705624 +---------------------- ---------------------------------------------------------- +Time: 66.602s Load: 1.195s, Pack+Encode: 33.500s, Decode+Unpack: 31.907s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 233.7056 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00041-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00041-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.151s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,621,676B, BPFP=1.0294 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,631,600B, BPFP=1.0357 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,465,252B, BPFP=1.5648 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,411,416B, BPFP=1.5306 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,763,752B, BPFP=1.7543 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,758,992B, BPFP=1.7513 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,477,528B, BPFP=0.9379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,502,156B, BPFP=0.9535 +⌛️ [2/4] FRONTEND: Frontend time: 32.960s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00251883 4.27539570 + layer.9.1 0.14409062 0.35687456 + layer.19.0 0.12740102 7.82424414 + layer.19.1 0.12254588 5.91475755 + layer.29.0 4.25147928 42.44757729 + layer.29.1 4.25065697 39.38992424 + layer.39.0 9.21805114 1177.24707507 + layer.39.1 9.03214690 913.21018850 + ------------------------------------------------------------------------------------- + TOTAL 3.39361133 273.83325463 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16632372 +BPFP 1.3197 bits/point +EBPFP 1.3197 equivalent bits/point +MSE 273.833255 +---------------------- ---------------------------------------------------------- +Time: 65.567s Load: 1.151s, Pack+Encode: 32.960s, Decode+Unpack: 31.456s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 273.8333 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00042-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00042-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,781,364B, BPFP=1.1307 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,762,792B, BPFP=1.1189 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,520,468B, BPFP=1.5999 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,519,072B, BPFP=1.5990 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,845,836B, BPFP=1.8064 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,881,496B, BPFP=1.8290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,653,416B, BPFP=1.0495 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,704,536B, BPFP=1.0820 +⌛️ [2/4] FRONTEND: Frontend time: 33.794s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.952s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14550905 0.38422950 + layer.9.1 0.14590163 8.31750056 + layer.19.0 0.12839093 12.32541208 + layer.19.1 0.12422524 8.57922946 + layer.29.0 0.11695262 39.74268007 + layer.29.1 0.11389293 36.33282469 + layer.39.0 10.18180439 1488.23253169 + layer.39.1 10.42432323 1399.83652909 + ------------------------------------------------------------------------------------- + TOTAL 2.67262500 374.21886714 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17668980 +BPFP 1.4019 bits/point +EBPFP 1.4019 equivalent bits/point +MSE 374.218867 +---------------------- ---------------------------------------------------------- +Time: 66.829s Load: 1.082s, Pack+Encode: 33.794s, Decode+Unpack: 31.952s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 374.2189 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00620-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00620-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.229s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,665,720B, BPFP=1.0573 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,595,072B, BPFP=1.0125 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,450,564B, BPFP=1.5555 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,390,396B, BPFP=1.5173 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,819,884B, BPFP=1.7899 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,787,892B, BPFP=1.7696 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,511,316B, BPFP=0.9593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,525,968B, BPFP=0.9686 +⌛️ [2/4] FRONTEND: Frontend time: 32.670s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.107s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14509775 0.51220600 + layer.9.1 0.14508723 0.46067892 + layer.19.0 0.11633494 6.32381441 + layer.19.1 0.11804005 2.99982100 + layer.29.0 0.15409572 43.20886314 + layer.29.1 0.14997486 45.66524313 + layer.39.0 9.23291952 963.53128047 + layer.39.1 9.22304726 1132.16127722 + ------------------------------------------------------------------------------------- + TOTAL 2.41057467 274.35789804 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16746812 +BPFP 1.3288 bits/point +EBPFP 1.3288 equivalent bits/point +MSE 274.357898 +---------------------- ---------------------------------------------------------- +Time: 66.005s Load: 1.229s, Pack+Encode: 32.670s, Decode+Unpack: 32.107s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 274.3579 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00636-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00636-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.201s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,681,540B, BPFP=1.0674 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,700,860B, BPFP=1.0796 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,449,600B, BPFP=1.5549 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,471,856B, BPFP=1.5690 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,808,796B, BPFP=1.7829 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,832,376B, BPFP=1.7978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,540,624B, BPFP=0.9779 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,554,416B, BPFP=0.9867 +⌛️ [2/4] FRONTEND: Frontend time: 32.806s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497060 4.37349120 + layer.9.1 0.14492971 3.02973612 + layer.19.0 0.11929473 6.62625617 + layer.19.1 0.11869117 3.05096247 + layer.29.0 0.13715227 35.26860832 + layer.29.1 0.14278979 29.64214840 + layer.39.0 9.99110525 1205.31516087 + layer.39.1 10.01170034 1155.85391615 + ------------------------------------------------------------------------------------- + TOTAL 2.60132923 305.39503496 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17040068 +BPFP 1.3520 bits/point +EBPFP 1.3520 equivalent bits/point +MSE 305.395035 +---------------------- ---------------------------------------------------------- +Time: 65.619s Load: 1.201s, Pack+Encode: 32.806s, Decode+Unpack: 31.612s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 305.3950 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office-rgb_00637-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office-rgb_00637-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,548,076B, BPFP=0.9826 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,561,664B, BPFP=0.9913 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,353,760B, BPFP=1.4940 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,393,952B, BPFP=1.5196 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,746,576B, BPFP=1.7434 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,771,580B, BPFP=1.7593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,622,240B, BPFP=1.0297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,657,828B, BPFP=1.0523 +⌛️ [2/4] FRONTEND: Frontend time: 33.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.155s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14589856 0.34047822 + layer.9.1 0.03321603 0.34848388 + layer.19.0 0.11866178 5.58904968 + layer.19.1 0.11267978 7.78823479 + layer.29.0 0.10803594 37.38206603 + layer.29.1 0.10714094 38.67001391 + layer.39.0 11.58943751 1405.44491388 + layer.39.1 9.70079103 1568.97530062 + ------------------------------------------------------------------------------------- + TOTAL 2.73948269 383.06731763 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16655676 +BPFP 1.3215 bits/point +EBPFP 1.3215 equivalent bits/point +MSE 383.067318 +---------------------- ---------------------------------------------------------- +Time: 66.918s Load: 1.227s, Pack+Encode: 33.537s, Decode+Unpack: 32.155s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 383.0673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00410-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00410-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.149s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,504,052B, BPFP=0.9547 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,511,156B, BPFP=0.9592 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,363,384B, BPFP=1.5002 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,349,900B, BPFP=1.4916 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,677,008B, BPFP=1.6992 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,661,632B, BPFP=1.6895 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,396,408B, BPFP=0.8864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,401,596B, BPFP=0.8897 +⌛️ [2/4] FRONTEND: Frontend time: 33.109s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.566s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03308301 4.30117956 + layer.9.1 0.14566304 0.48634185 + layer.19.0 0.03810260 7.87810520 + layer.19.1 0.03780774 3.11239704 + layer.29.0 0.11592613 50.21236188 + layer.29.1 0.11717217 47.34262776 + layer.39.0 9.98032847 780.35391615 + layer.39.1 9.70849498 977.53574911 + ------------------------------------------------------------------------------------- + TOTAL 2.52207227 233.90283482 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15865136 +BPFP 1.2588 bits/point +EBPFP 1.2588 equivalent bits/point +MSE 233.902835 +---------------------- ---------------------------------------------------------- +Time: 65.824s Load: 1.149s, Pack+Encode: 33.109s, Decode+Unpack: 31.566s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 233.9028 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00411-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00411-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.139s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,470,224B, BPFP=0.9332 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,437,740B, BPFP=0.9126 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,374,520B, BPFP=1.5072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,329,728B, BPFP=1.4788 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,518,176B, BPFP=1.5984 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,490,376B, BPFP=1.5808 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,232,404B, BPFP=0.7823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,223,412B, BPFP=0.7766 +⌛️ [2/4] FRONTEND: Frontend time: 32.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.903s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14555504 4.35448616 + layer.9.1 0.14557384 0.30427646 + layer.19.0 0.03995539 3.67505097 + layer.19.1 0.04542811 6.36559999 + layer.29.0 0.12033866 44.40584884 + layer.29.1 0.13252172 40.45896978 + layer.39.0 10.37566776 747.80874228 + layer.39.1 9.84188447 696.23448164 + ------------------------------------------------------------------------------------- + TOTAL 2.60586562 192.95093201 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15076580 +BPFP 1.1962 bits/point +EBPFP 1.1962 equivalent bits/point +MSE 192.950932 +---------------------- ---------------------------------------------------------- +Time: 65.672s Load: 1.139s, Pack+Encode: 32.630s, Decode+Unpack: 31.903s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 192.9509 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00412-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00412-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,707,476B, BPFP=1.0838 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,643,760B, BPFP=1.0434 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,465,628B, BPFP=1.5651 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,426,116B, BPFP=1.5400 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,839,484B, BPFP=1.8024 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,821,604B, BPFP=1.7910 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,750,136B, BPFP=1.1109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,758,380B, BPFP=1.1161 +⌛️ [2/4] FRONTEND: Frontend time: 32.816s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.961s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14470203 4.22421456 + layer.9.1 0.14481130 4.34151885 + layer.19.0 0.11257574 11.65503890 + layer.19.1 0.11422884 7.32633754 + layer.29.0 0.10456927 41.81202013 + layer.29.1 0.10551051 35.33869231 + layer.39.0 10.36536069 1835.71270718 + layer.39.1 11.81531702 1412.07003575 + ------------------------------------------------------------------------------------- + TOTAL 2.86338443 419.06007065 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17412584 +BPFP 1.3816 bits/point +EBPFP 1.3816 equivalent bits/point +MSE 419.060071 +---------------------- ---------------------------------------------------------- +Time: 65.958s Load: 1.181s, Pack+Encode: 32.816s, Decode+Unpack: 31.961s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 419.0601 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/office_kitchen-rgb_00413-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/office_kitchen-rgb_00413-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,632,804B, BPFP=1.0364 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,639,552B, BPFP=1.0407 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,482,916B, BPFP=1.5760 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,445,028B, BPFP=1.5520 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,663,940B, BPFP=1.6909 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,645,812B, BPFP=1.6794 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,396,528B, BPFP=0.8864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,387,376B, BPFP=0.8806 +⌛️ [2/4] FRONTEND: Frontend time: 33.102s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.095s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14530003 4.39477563 + layer.9.1 0.14546206 0.37812654 + layer.19.0 0.11891763 4.32618997 + layer.19.1 0.11677460 3.47016168 + layer.29.0 4.29725807 48.73988971 + layer.29.1 4.29692800 48.08176085 + layer.39.0 11.61914761 1103.07962301 + layer.39.1 11.22064282 914.52307442 + ------------------------------------------------------------------------------------- + TOTAL 3.99505385 265.87420023 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16293956 +BPFP 1.2928 bits/point +EBPFP 1.2928 equivalent bits/point +MSE 265.874200 +---------------------- ---------------------------------------------------------- +Time: 66.260s Load: 1.062s, Pack+Encode: 33.102s, Decode+Unpack: 32.095s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 265.8742 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00433-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00433-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.197s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,448,644B, BPFP=0.9195 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,451,272B, BPFP=0.9212 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,237,116B, BPFP=1.4200 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,223,860B, BPFP=1.4116 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,457,736B, BPFP=1.5600 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,465,932B, BPFP=1.5652 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,456,856B, BPFP=0.9247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,416,740B, BPFP=0.8993 +⌛️ [2/4] FRONTEND: Frontend time: 33.400s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.668s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.67106792 0.47053718 + layer.9.1 2.67195307 0.72662169 + layer.19.0 0.08237472 9.74571544 + layer.19.1 0.08192194 8.11449489 + layer.29.0 0.11152953 37.79888081 + layer.29.1 0.11703055 34.67045570 + layer.39.0 163.01811830 1309.01836204 + layer.39.1 58.15221299 1087.86715957 + ------------------------------------------------------------------------------------- + TOTAL 28.36327613 311.05152841 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15158156 +BPFP 1.2027 bits/point +EBPFP 1.2027 equivalent bits/point +MSE 311.051528 +---------------------- ---------------------------------------------------------- +Time: 66.265s Load: 1.197s, Pack+Encode: 33.400s, Decode+Unpack: 31.668s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 311.0515 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00443-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00443-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,694,960B, BPFP=1.0759 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,709,544B, BPFP=1.0851 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,403,996B, BPFP=1.5259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,422,204B, BPFP=1.5375 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,646,328B, BPFP=1.6798 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,696,960B, BPFP=1.7119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,430,756B, BPFP=0.9082 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,417,796B, BPFP=0.8999 +⌛️ [2/4] FRONTEND: Frontend time: 33.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.922s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14620710 0.47961005 + layer.9.1 0.14642976 8.35077731 + layer.19.0 0.11726453 7.07089584 + layer.19.1 0.11958517 12.90138528 + layer.29.0 0.10693079 35.28263122 + layer.29.1 0.10826971 37.69081085 + layer.39.0 43.01306569 1202.63259669 + layer.39.1 17.12450997 1044.47643809 + ------------------------------------------------------------------------------------- + TOTAL 7.61028284 293.61064317 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16422544 +BPFP 1.3030 bits/point +EBPFP 1.3030 equivalent bits/point +MSE 293.610643 +---------------------- ---------------------------------------------------------- +Time: 66.729s Load: 1.213s, Pack+Encode: 33.594s, Decode+Unpack: 31.922s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 293.6106 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00444-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00444-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.211s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,491,680B, BPFP=0.9468 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,471,028B, BPFP=0.9337 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,250,832B, BPFP=1.4287 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,198,936B, BPFP=1.3958 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,474,852B, BPFP=1.5709 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,472,640B, BPFP=1.5695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,321,252B, BPFP=0.8387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,328,900B, BPFP=0.8435 +⌛️ [2/4] FRONTEND: Frontend time: 33.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.798s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03173930 0.33924780 + layer.9.1 0.03345565 4.34268203 + layer.19.0 3.26068347 6.89686015 + layer.19.1 3.26087326 3.44282142 + layer.29.0 4.24610771 43.62773704 + layer.29.1 4.24089229 47.75285383 + layer.39.0 8.81319124 783.74861878 + layer.39.1 8.71779153 852.45580110 + ------------------------------------------------------------------------------------- + TOTAL 4.07559181 217.82582777 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15010120 +BPFP 1.1910 bits/point +EBPFP 1.1910 equivalent bits/point +MSE 217.825828 +---------------------- ---------------------------------------------------------- +Time: 66.587s Load: 1.211s, Pack+Encode: 33.578s, Decode+Unpack: 31.798s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 217.8258 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00445-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00445-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.225s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,565,520B, BPFP=0.9937 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,593,332B, BPFP=1.0114 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,400,292B, BPFP=1.5236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,400,412B, BPFP=1.5237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,787,820B, BPFP=1.7696 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,781,348B, BPFP=1.7655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,484,196B, BPFP=0.9421 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,542,168B, BPFP=0.9789 +⌛️ [2/4] FRONTEND: Frontend time: 33.681s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.135s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00078599 3.08212043 + layer.9.1 0.00079117 8.36006497 + layer.19.0 0.00795310 3.80133094 + layer.19.1 0.00811505 8.11295753 + layer.29.0 4.25797468 44.62825500 + layer.29.1 4.25504309 44.28714556 + layer.39.0 81.06806549 1164.37577186 + layer.39.1 44.82015254 1486.25154371 + ------------------------------------------------------------------------------------- + TOTAL 16.80236014 345.36239875 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16555088 +BPFP 1.3135 bits/point +EBPFP 1.3135 equivalent bits/point +MSE 345.362399 +---------------------- ---------------------------------------------------------- +Time: 67.041s Load: 1.225s, Pack+Encode: 33.681s, Decode+Unpack: 32.135s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 345.3624 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00446-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00446-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.265s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,578,568B, BPFP=1.0020 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,597,820B, BPFP=1.0142 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,407,428B, BPFP=1.5281 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,434,128B, BPFP=1.5451 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,807,212B, BPFP=1.7819 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,798,200B, BPFP=1.7762 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,541,464B, BPFP=0.9784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,529,316B, BPFP=0.9707 +⌛️ [2/4] FRONTEND: Frontend time: 32.938s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.615s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00082351 4.32281944 + layer.9.1 0.02968625 0.50562975 + layer.19.0 0.00841222 6.21505322 + layer.19.1 0.03743129 2.71844056 + layer.29.0 4.28408194 47.18006073 + layer.29.1 4.28564945 43.57693675 + layer.39.0 8.35370986 1343.95896978 + layer.39.1 8.52557915 1177.55532987 + ------------------------------------------------------------------------------------- + TOTAL 3.19067171 328.25415501 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16694136 +BPFP 1.3246 bits/point +EBPFP 1.3246 equivalent bits/point +MSE 328.254155 +---------------------- ---------------------------------------------------------- +Time: 65.819s Load: 1.265s, Pack+Encode: 32.938s, Decode+Unpack: 31.615s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 328.2542 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/playroom-rgb_00447-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/playroom-rgb_00447-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.165s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,558,408B, BPFP=0.9892 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,603,700B, BPFP=1.0179 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,375,068B, BPFP=1.5076 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,405,472B, BPFP=1.5269 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,620,456B, BPFP=1.6633 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,639,860B, BPFP=1.6757 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,413,344B, BPFP=0.8971 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,401,332B, BPFP=0.8895 +⌛️ [2/4] FRONTEND: Frontend time: 32.853s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.639s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14529990 4.33016191 + layer.9.1 0.14524076 3.01057874 + layer.19.0 0.03780325 7.79405735 + layer.19.1 0.03783790 2.79249650 + layer.29.0 4.32098184 33.70821062 + layer.29.1 4.32100596 36.31108832 + layer.39.0 9.32673680 1088.38625284 + layer.39.1 9.31823369 1118.29793630 + ------------------------------------------------------------------------------------- + TOTAL 3.45664251 286.82884782 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16017640 +BPFP 1.2709 bits/point +EBPFP 1.2709 equivalent bits/point +MSE 286.828848 +---------------------- ---------------------------------------------------------- +Time: 65.657s Load: 1.165s, Pack+Encode: 32.853s, Decode+Unpack: 31.639s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 286.8288 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00461-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00461-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.191s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,570,188B, BPFP=0.9967 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,558,828B, BPFP=0.9895 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,358,112B, BPFP=1.4968 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,327,740B, BPFP=1.4775 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,642,864B, BPFP=1.6776 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,672,172B, BPFP=1.6962 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,458,384B, BPFP=0.9257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,465,624B, BPFP=0.9303 +⌛️ [2/4] FRONTEND: Frontend time: 33.413s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.785s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14497286 0.44577670 + layer.9.1 0.14497296 3.07741186 + layer.19.0 0.03962668 6.32048579 + layer.19.1 0.11751332 5.99637431 + layer.29.0 0.14529291 36.13029127 + layer.29.1 0.16241527 35.80119892 + layer.39.0 11.40179406 875.64259019 + layer.39.1 13.03458244 1082.17695808 + ------------------------------------------------------------------------------------- + TOTAL 3.14889631 255.69888589 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16053912 +BPFP 1.2738 bits/point +EBPFP 1.2738 equivalent bits/point +MSE 255.698886 +---------------------- ---------------------------------------------------------- +Time: 66.389s Load: 1.191s, Pack+Encode: 33.413s, Decode+Unpack: 31.785s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 255.6989 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00462-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00462-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.188s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,684,960B, BPFP=1.0695 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,664,364B, BPFP=1.0565 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,475,140B, BPFP=1.5711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,451,504B, BPFP=1.5561 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,794,576B, BPFP=1.7739 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,792,676B, BPFP=1.7727 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,544,852B, BPFP=0.9806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,553,840B, BPFP=0.9863 +⌛️ [2/4] FRONTEND: Frontend time: 33.729s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.723s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14513344 0.59422424 + layer.9.1 0.03283094 4.27898870 + layer.19.0 0.11544709 11.95049587 + layer.19.1 0.11326018 6.26707223 + layer.29.0 0.14483232 40.22677324 + layer.29.1 0.14672551 36.27905630 + layer.39.0 10.02784076 949.66152096 + layer.39.1 15.62606130 1039.23700032 + ------------------------------------------------------------------------------------- + TOTAL 3.29401644 261.06189148 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16961912 +BPFP 1.3458 bits/point +EBPFP 1.3458 equivalent bits/point +MSE 261.061891 +---------------------- ---------------------------------------------------------- +Time: 66.640s Load: 1.188s, Pack+Encode: 33.729s, Decode+Unpack: 31.723s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 261.0619 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00463-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00463-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.182s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,745,896B, BPFP=1.1082 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,758,604B, BPFP=1.1163 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,580,008B, BPFP=1.6377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,581,808B, BPFP=1.6388 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,883,048B, BPFP=1.8300 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,880,292B, BPFP=1.8283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,622,712B, BPFP=1.0300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,632,772B, BPFP=1.0364 +⌛️ [2/4] FRONTEND: Frontend time: 33.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.146s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492303 4.28440629 + layer.9.1 0.14484742 4.29438964 + layer.19.0 0.11740684 7.15477738 + layer.19.1 0.11489933 5.64328524 + layer.29.0 0.12072669 36.93602484 + layer.29.1 0.12118037 32.93098747 + layer.39.0 10.74778980 1065.54468638 + layer.39.1 11.83662176 1187.60749106 + ------------------------------------------------------------------------------------- + TOTAL 2.91854940 293.04950604 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17685140 +BPFP 1.4032 bits/point +EBPFP 1.4032 equivalent bits/point +MSE 293.049506 +---------------------- ---------------------------------------------------------- +Time: 66.540s Load: 1.182s, Pack+Encode: 33.212s, Decode+Unpack: 32.146s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 293.0495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00464-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00464-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,780,952B, BPFP=1.1305 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,775,524B, BPFP=1.1270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,559,288B, BPFP=1.6245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,557,936B, BPFP=1.6236 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,867,180B, BPFP=1.8199 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,879,952B, BPFP=1.8280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,648,588B, BPFP=1.0464 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,616,340B, BPFP=1.0260 +⌛️ [2/4] FRONTEND: Frontend time: 33.391s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.899s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14465000 0.47844648 + layer.9.1 0.14489275 0.47740640 + layer.19.0 0.11978787 7.05995021 + layer.19.1 0.12819003 6.40139416 + layer.29.0 0.12519148 36.42941329 + layer.29.1 0.13018718 33.70190882 + layer.39.0 10.77894586 1232.97684433 + layer.39.1 10.25834823 1165.10204745 + ------------------------------------------------------------------------------------- + TOTAL 2.72877418 310.32842639 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17685760 +BPFP 1.4033 bits/point +EBPFP 1.4033 equivalent bits/point +MSE 310.328426 +---------------------- ---------------------------------------------------------- +Time: 66.471s Load: 1.181s, Pack+Encode: 33.391s, Decode+Unpack: 31.899s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 310.3284 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/reception_room-rgb_00465-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/reception_room-rgb_00465-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,593,176B, BPFP=1.0113 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,596,588B, BPFP=1.0134 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,341,916B, BPFP=1.4865 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,317,976B, BPFP=1.4713 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,610,008B, BPFP=1.6567 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,611,392B, BPFP=1.6576 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,427,872B, BPFP=0.9063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,460,480B, BPFP=0.9270 +⌛️ [2/4] FRONTEND: Frontend time: 33.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.120s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14553812 3.02265231 + layer.9.1 0.14559401 8.42230790 + layer.19.0 0.04492324 8.58087854 + layer.19.1 0.04213941 8.35963461 + layer.29.0 4.25320263 37.32889178 + layer.29.1 4.25391672 40.85308336 + layer.39.0 8.72311137 791.57807930 + layer.39.1 8.87262096 733.94003900 + ------------------------------------------------------------------------------------- + TOTAL 3.31013081 204.01069585 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 15959408 +BPFP 1.2663 bits/point +EBPFP 1.2663 equivalent bits/point +MSE 204.010696 +---------------------- ---------------------------------------------------------- +Time: 66.862s Load: 1.213s, Pack+Encode: 33.529s, Decode+Unpack: 32.120s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 204.0107 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00471-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00471-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.220s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,648,172B, BPFP=1.0462 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,671,656B, BPFP=1.0611 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,366,036B, BPFP=1.5018 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,378,988B, BPFP=1.5101 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,688,008B, BPFP=1.7062 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,687,084B, BPFP=1.7056 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,467,704B, BPFP=0.9316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,473,984B, BPFP=0.9356 +⌛️ [2/4] FRONTEND: Frontend time: 32.941s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.691s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14518122 4.33226928 + layer.9.1 0.14529820 3.01133187 + layer.19.0 0.11833418 6.34063719 + layer.19.1 0.12038008 8.81706702 + layer.29.0 4.31360161 38.33415004 + layer.29.1 4.31792870 37.67468313 + layer.39.0 9.40764201 1048.89600260 + layer.39.1 11.30764416 910.54103022 + ------------------------------------------------------------------------------------- + TOTAL 3.73450127 257.24339642 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16381632 +BPFP 1.2998 bits/point +EBPFP 1.2998 equivalent bits/point +MSE 257.243396 +---------------------- ---------------------------------------------------------- +Time: 65.851s Load: 1.220s, Pack+Encode: 32.941s, Decode+Unpack: 31.691s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 257.2434 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00472-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00472-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.192s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,825,568B, BPFP=1.1588 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,789,368B, BPFP=1.1358 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,566,632B, BPFP=1.6292 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,544,088B, BPFP=1.6149 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,875,708B, BPFP=1.8254 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,883,704B, BPFP=1.8304 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,568,108B, BPFP=0.9954 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,568,992B, BPFP=0.9959 +⌛️ [2/4] FRONTEND: Frontend time: 33.439s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.925s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00315138 0.72984059 + layer.9.1 0.00505826 0.34857723 + layer.19.0 0.09147678 3.07902031 + layer.19.1 0.09143778 2.75490947 + layer.29.0 0.11015094 31.91819599 + layer.29.1 0.11338039 28.86961224 + layer.39.0 9.14784464 1144.69694508 + layer.39.1 8.98944348 993.59481638 + ------------------------------------------------------------------------------------- + TOTAL 2.31899296 275.74898966 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17622168 +BPFP 1.3982 bits/point +EBPFP 1.3982 equivalent bits/point +MSE 275.748990 +---------------------- ---------------------------------------------------------- +Time: 66.556s Load: 1.192s, Pack+Encode: 33.439s, Decode+Unpack: 31.925s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 275.7490 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00475-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,807,228B, BPFP=1.1471 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,825,860B, BPFP=1.1590 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,584,984B, BPFP=1.6408 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,590,488B, BPFP=1.6443 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,993,352B, BPFP=1.9000 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,981,664B, BPFP=1.8926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,769,672B, BPFP=1.1233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,709,676B, BPFP=1.0852 +⌛️ [2/4] FRONTEND: Frontend time: 33.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.957s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14591913 4.42587741 + layer.9.1 0.03347605 4.33501299 + layer.19.0 0.12173996 6.29991863 + layer.19.1 0.12099332 1.85179641 + layer.29.0 0.11078974 20.54516625 + layer.29.1 0.11776269 23.18354424 + layer.39.0 10.17800795 1697.34725382 + layer.39.1 9.88744998 1546.75203120 + ------------------------------------------------------------------------------------- + TOTAL 2.58951735 413.09257512 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 18262924 +BPFP 1.4490 bits/point +EBPFP 1.4490 equivalent bits/point +MSE 413.092575 +---------------------- ---------------------------------------------------------- +Time: 66.632s Load: 1.174s, Pack+Encode: 33.502s, Decode+Unpack: 31.957s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 413.0926 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00476-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,539,132B, BPFP=0.9770 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,596,944B, BPFP=1.0137 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,399,920B, BPFP=1.5233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,391,776B, BPFP=1.5182 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,828,368B, BPFP=1.7953 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,815,724B, BPFP=1.7873 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,366,212B, BPFP=0.8672 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,403,904B, BPFP=0.8911 +⌛️ [2/4] FRONTEND: Frontend time: 32.888s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.947s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.66405266 0.47619236 + layer.9.1 2.66543197 4.47268450 + layer.19.0 3.22131407 7.75205976 + layer.19.1 3.22426883 7.26102941 + layer.29.0 4.27224607 35.78563231 + layer.29.1 4.27784520 38.08904270 + layer.39.0 8.94937744 857.51909327 + layer.39.1 8.82170070 904.47863178 + ------------------------------------------------------------------------------------- + TOTAL 4.76202962 231.97929576 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16341980 +BPFP 1.2966 bits/point +EBPFP 1.2966 equivalent bits/point +MSE 231.979296 +---------------------- ---------------------------------------------------------- +Time: 66.044s Load: 1.209s, Pack+Encode: 32.888s, Decode+Unpack: 31.947s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 231.9793 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00643-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.180s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,642,732B, BPFP=1.0427 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,663,616B, BPFP=1.0560 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,433,624B, BPFP=1.5447 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,423,104B, BPFP=1.5381 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,836,904B, BPFP=1.8007 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,852,228B, BPFP=1.8105 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,539,948B, BPFP=0.9775 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,570,424B, BPFP=0.9968 +⌛️ [2/4] FRONTEND: Frontend time: 33.412s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.761s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00104424 4.27171257 + layer.9.1 0.00091568 3.11042964 + layer.19.0 0.08171424 16.26730074 + layer.19.1 0.08373584 2.54835841 + layer.29.0 4.26071267 38.79917026 + layer.29.1 4.26438533 38.38354373 + layer.39.0 8.39843369 964.62114072 + layer.39.1 8.51949380 1244.30914852 + ------------------------------------------------------------------------------------- + TOTAL 3.20130444 289.03885057 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16962580 +BPFP 1.3459 bits/point +EBPFP 1.3459 equivalent bits/point +MSE 289.038851 +---------------------- ---------------------------------------------------------- +Time: 66.353s Load: 1.180s, Pack+Encode: 33.412s, Decode+Unpack: 31.761s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 289.0389 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study-rgb_00644-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.196s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,734,444B, BPFP=1.1009 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,740,820B, BPFP=1.1050 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,454,604B, BPFP=1.5581 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,493,232B, BPFP=1.5826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,873,120B, BPFP=1.8237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,869,760B, BPFP=1.8216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,662,376B, BPFP=1.0552 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,628,284B, BPFP=1.0336 +⌛️ [2/4] FRONTEND: Frontend time: 32.707s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14622840 0.35215571 + layer.9.1 0.03344178 4.34145982 + layer.19.0 0.12675888 11.19321021 + layer.19.1 0.12382618 7.08721017 + layer.29.0 0.12223263 29.03258044 + layer.29.1 0.12797405 31.45763172 + layer.39.0 10.69978368 1471.64348391 + layer.39.1 8.63538768 1074.37049074 + ------------------------------------------------------------------------------------- + TOTAL 2.50195416 328.68477784 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 17456640 +BPFP 1.3851 bits/point +EBPFP 1.3851 equivalent bits/point +MSE 328.684778 +---------------------- ---------------------------------------------------------- +Time: 65.992s Load: 1.196s, Pack+Encode: 32.707s, Decode+Unpack: 32.089s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 328.6848 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00272-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst... + +Original data structure: +root: [dict] with 4 keys + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.213s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3077, 4096]), torch.float32 + layer.9.1: torch.Size([3077, 4096]), torch.float32 + layer.19.0: torch.Size([3077, 4096]), torch.float32 + layer.19.1: torch.Size([3077, 4096]), torch.float32 + layer.29.0: torch.Size([3077, 4096]), torch.float32 + layer.29.1: torch.Size([3077, 4096]), torch.float32 + layer.39.0: torch.Size([3077, 4096]), torch.float32 + layer.39.1: torch.Size([3077, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3077, 4096]) -> torch.Size([1, 1, 3077, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,695,820B, BPFP=1.0764 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,695,052B, BPFP=1.0759 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,406,996B, BPFP=1.5278 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,413,028B, BPFP=1.5317 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,690,060B, BPFP=1.7075 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,664,664B, BPFP=1.6914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,493,412B, BPFP=0.9479 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,494,968B, BPFP=0.9489 +⌛️ [2/4] FRONTEND: Frontend time: 33.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3077, 4096]) + layer.9.1: torch.Size([1, 3077, 4096]) + layer.19.0: torch.Size([1, 3077, 4096]) + layer.19.1: torch.Size([1, 3077, 4096]) + layer.29.0: torch.Size([1, 3077, 4096]) + layer.29.1: torch.Size([1, 3077, 4096]) + layer.39.0: torch.Size([1, 3077, 4096]) + layer.39.1: torch.Size([1, 3077, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14492994 8.32248652 + layer.9.1 0.14498602 0.58113387 + layer.19.0 0.12957112 6.20183709 + layer.19.1 0.13054295 7.73763063 + layer.29.0 0.16610158 33.50622816 + layer.29.1 0.14872770 35.80611950 + layer.39.0 16.52878844 1260.36951576 + layer.39.1 24.55764797 1209.08011050 + ------------------------------------------------------------------------------------- + TOTAL 5.24391197 320.20063275 + (elements=100,827,136) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 100827136 +Total Bytes 16554000 +BPFP 1.3135 bits/point +EBPFP 1.3135 equivalent bits/point +MSE 320.200633 +---------------------- ---------------------------------------------------------- +Time: 66.878s Load: 1.213s, Pack+Encode: 33.600s, Decode+Unpack: 32.065s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3077, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 320.2006 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Dep-NYUDv2Test-4lvl-100Features/study_room-rgb_00278-wflip.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.3038 bits/point +Avg EBPFP 1.3038 equivalent bits/point +Avg MSE 304.864781 +Avg Time 66.419s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..0ed27761c4e3b146ab2e8ba3ca01f79116aba60b --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00475-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:688ca13834e12973e5ce62e65a1b4fe5cac2b3cd15e9d908c197edea1baa5253 +size 113208760 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..507aeeb3199725b7fcab2487e5b3deacdb84d615 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00476-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e44d1229395ba3bfe460dd874beb09b528f60e3b197123c4beff5c41c68bf861 +size 113804560 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..757817a7977fe5a263247268c9685c14718cc3e2 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00643-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eddb77bb7204fcc18751d50da775fd7786ea6a9688692769681702d275a4c591 +size 112850023 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..6902124614a74c0a5c6fc7007e46d0088acb9917 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study-rgb_00644-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d5da0d665474ba51c28b55da1cdaa2195e70a5cfcf30332c81e024128d33b45 +size 112462586 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..b7d14c914166d4c38e25a355bf3b6a5dce824e09 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00272-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f664c5932467c7582293f54868f48da87abb1287c4a2927c3b9837d12dc62ad +size 112972439 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst new file mode 100644 index 0000000000000000000000000000000000000000..e6e9b426a971c149e6f7cc2cab05ec292051ff85 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/nyudv2_test/study_room-rgb_00278-wflip.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3376eb2ad48afc98f9b83f1c564aab433d329b00d46ed1b16be0f0827d32d177 +size 112878668