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b/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000880-stackedpatches.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0211e2181a82e7bb86872166aabc6042f5862fc662edd81b01db7e955044b7a +size 94229785 diff --git a/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst b/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst new file mode 100644 index 0000000000000000000000000000000000000000..67e56cdb2c919304c981cb405f68afc747832f10 --- /dev/null +++ b/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69bb33fc56cfb91e8e212fc67e211a8ef692b9141d2a83648ec4762426b6671a +size 91330765 diff --git a/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..2bfc38a944488bebe0502bf677f57d78b5a7e546 --- /dev/null +++ b/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_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/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 405 +Loaded hyperprior-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 hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.294s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,796B, BPFP=0.0384 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,864B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,528B, BPFP=0.0513 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,080B, BPFP=0.0510 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,980B, BPFP=0.0628 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,380B, BPFP=0.0630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 89,232B, BPFP=0.0555 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 90,352B, BPFP=0.0562 +⌛️ [2/4] FRONTEND: Frontend time: 8.127s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11100285 6.12334473 + layer.9.1 0.11103876 6.15206518 + layer.19.0 0.02553116 28.04892451 + layer.19.1 0.10833414 28.57361061 + layer.29.0 0.30844607 337.86326011 + layer.29.1 0.33610574 338.66475645 + layer.39.0 10.03071710 15924.58962114 + layer.39.1 10.11984639 16212.94237504 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 4110.36974472 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 669212 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 4110.369745 +---------------------- --------------------------------------------------------- +Time: 22.518s Load: 1.294s, Pack+Encode: 8.127s, Decode+Unpack: 13.097s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4110.3697 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000001-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,636B, BPFP=0.0371 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,608B, BPFP=0.0371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,564B, BPFP=0.0501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,308B, BPFP=0.0499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,456B, BPFP=0.0643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,284B, BPFP=0.0642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,008B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,988B, BPFP=0.0616 +⌛️ [2/4] FRONTEND: Frontend time: 7.798s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.741s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61021196 5.86310839 + layer.9.1 2.61901253 5.88831247 + layer.19.0 3.15140481 25.70534364 + layer.19.1 3.16250889 26.14534085 + layer.29.0 4.15625404 289.35776823 + layer.29.1 4.15938147 292.44539955 + layer.39.0 10.95910936 18460.15027061 + layer.39.1 9.06533984 18665.36517033 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 4721.36508926 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 684852 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 4721.365089 +---------------------- --------------------------------------------------------- +Time: 21.852s Load: 1.313s, Pack+Encode: 7.798s, Decode+Unpack: 12.741s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4721.3651 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000045-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.307s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,924B, BPFP=0.0373 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,068B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,136B, BPFP=0.0486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,408B, BPFP=0.0488 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,052B, BPFP=0.0597 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 96,036B, BPFP=0.0597 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,124B, BPFP=0.0585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,812B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 8.093s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.090s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 6.19518130 + layer.9.1 0.14253284 6.19937607 + layer.19.0 0.09744245 28.65048701 + layer.19.1 0.13747554 28.93959179 + layer.29.0 4.19766265 301.96756606 + layer.29.1 4.20130152 302.67790911 + layer.39.0 38.53896798 20673.98153454 + layer.39.1 35.26563495 20840.55396371 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 5273.64570120 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 656560 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 5273.645701 +---------------------- --------------------------------------------------------- +Time: 22.489s Load: 1.307s, Pack+Encode: 8.093s, Decode+Unpack: 13.090s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5273.6457 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000064-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.321s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,560B, BPFP=0.0370 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,932B, BPFP=0.0366 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,392B, BPFP=0.0506 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,628B, BPFP=0.0508 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,684B, BPFP=0.0632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,472B, BPFP=0.0631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,160B, BPFP=0.0617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,248B, BPFP=0.0611 +⌛️ [2/4] FRONTEND: Frontend time: 7.729s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.648s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 5.95302737 + layer.9.1 0.03225276 5.87807053 + layer.19.0 0.11899935 27.59691380 + layer.19.1 0.11456829 27.45911433 + layer.29.0 0.13249551 305.99162289 + layer.29.1 0.12471250 307.76987822 + layer.39.0 10.78219516 19714.01209806 + layer.39.1 9.99374328 19482.89971347 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 4984.69505483 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 682076 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 4984.695055 +---------------------- --------------------------------------------------------- +Time: 21.698s Load: 1.321s, Pack+Encode: 7.729s, Decode+Unpack: 12.648s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4984.6951 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000092-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,608B, BPFP=0.0358 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,632B, BPFP=0.0358 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,352B, BPFP=0.0500 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,228B, BPFP=0.0499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 105,876B, BPFP=0.0658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 104,240B, BPFP=0.0648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 100,868B, BPFP=0.0627 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,088B, BPFP=0.0610 +⌛️ [2/4] FRONTEND: Frontend time: 7.845s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.809s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 5.78451143 + layer.9.1 0.03227402 5.77238228 + layer.19.0 3.18865969 25.72764943 + layer.19.1 3.19251184 25.41499522 + layer.29.0 0.19572780 331.92611827 + layer.29.1 0.14992644 323.17639685 + layer.39.0 12.23891426 19093.52690226 + layer.39.1 9.64680585 19045.63514804 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 4857.12051297 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 684892 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 4857.120513 +---------------------- --------------------------------------------------------- +Time: 21.970s Load: 1.316s, Pack+Encode: 7.845s, Decode+Unpack: 12.809s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4857.1205 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000096-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,208B, BPFP=0.0362 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,452B, BPFP=0.0357 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,092B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,836B, BPFP=0.0490 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,324B, BPFP=0.0630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,236B, BPFP=0.0642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,124B, BPFP=0.0591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,044B, BPFP=0.0603 +⌛️ [2/4] FRONTEND: Frontend time: 7.698s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.711s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 5.86429481 + layer.9.1 0.14248663 5.85246662 + layer.19.0 0.04071400 26.37773102 + layer.19.1 0.03715074 26.15596645 + layer.29.0 4.22673132 320.12533827 + layer.29.1 4.22861263 320.57674706 + layer.39.0 10.70292353 17861.08373130 + layer.39.1 9.44238934 18096.75899395 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 4582.84940868 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 670316 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 4582.849409 +---------------------- --------------------------------------------------------- +Time: 21.654s Load: 1.245s, Pack+Encode: 7.698s, Decode+Unpack: 12.711s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4582.8494 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000133-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,120B, BPFP=0.0380 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,128B, BPFP=0.0386 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,756B, BPFP=0.0490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,124B, BPFP=0.0492 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,744B, BPFP=0.0614 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,196B, BPFP=0.0617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,196B, BPFP=0.0598 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,208B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 7.837s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 6.21873197 + layer.9.1 0.14203072 6.22975864 + layer.19.0 0.04969746 28.93656479 + layer.19.1 0.04852902 28.48840437 + layer.29.0 0.13952979 321.97614215 + layer.29.1 0.11857529 315.37756686 + layer.39.0 52.16041866 20300.97421203 + layer.39.1 64.85207736 20637.08882521 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 5205.66127575 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 672472 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 5205.661276 +---------------------- --------------------------------------------------------- +Time: 22.113s Load: 1.235s, Pack+Encode: 7.837s, Decode+Unpack: 13.041s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5205.6613 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000196-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,876B, BPFP=0.0372 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,784B, BPFP=0.0372 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,512B, BPFP=0.0507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,020B, BPFP=0.0504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,804B, BPFP=0.0633 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,240B, BPFP=0.0636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,352B, BPFP=0.0599 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 96,432B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 7.698s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.747s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 6.01404807 + layer.9.1 0.14255715 6.00569646 + layer.19.0 0.12077588 28.10248776 + layer.19.1 0.12364273 27.67553128 + layer.29.0 4.20710867 312.20075215 + layer.29.1 4.21108798 312.35092327 + layer.39.0 8.84959445 19603.19261382 + layer.39.1 9.12830806 19399.34161095 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 4961.86045797 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 679020 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 4961.860458 +---------------------- --------------------------------------------------------- +Time: 21.691s Load: 1.246s, Pack+Encode: 7.698s, Decode+Unpack: 12.747s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4961.8605 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000268-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,780B, BPFP=0.0372 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,308B, BPFP=0.0369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 76,720B, BPFP=0.0477 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 75,864B, BPFP=0.0472 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 95,372B, BPFP=0.0593 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 94,096B, BPFP=0.0585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,660B, BPFP=0.0589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,568B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 8.153s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.128s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 6.15801471 + layer.9.1 0.14262173 6.19581741 + layer.19.0 0.13202983 28.16980311 + layer.19.1 0.12978742 28.30391645 + layer.29.0 0.12169007 299.10096307 + layer.29.1 0.13371499 296.92842646 + layer.39.0 71.22791309 19474.57752308 + layer.39.1 35.82807525 19519.23845909 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 4957.33411542 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 649368 +BPFP 0.0505 bits/point +EBPFP 0.0505 equivalent bits/point +MSE 4957.334115 +---------------------- --------------------------------------------------------- +Time: 22.590s Load: 1.309s, Pack+Encode: 8.153s, Decode+Unpack: 13.128s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4957.3341 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000315-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.297s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,860B, BPFP=0.0360 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,208B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,660B, BPFP=0.0495 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,248B, BPFP=0.0499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,404B, BPFP=0.0637 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,152B, BPFP=0.0641 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,288B, BPFP=0.0617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,520B, BPFP=0.0619 +⌛️ [2/4] FRONTEND: Frontend time: 7.753s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.815s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 5.92905698 + layer.9.1 0.14121198 5.93269460 + layer.19.0 0.08207523 27.48069385 + layer.19.1 0.11558007 27.27131835 + layer.29.0 0.16338114 322.27560888 + layer.29.1 0.15213004 318.77127109 + layer.39.0 27.31461666 21655.74021012 + layer.39.1 28.69002706 20886.36994588 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 5406.22134997 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 680340 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 5406.221350 +---------------------- --------------------------------------------------------- +Time: 21.865s Load: 1.297s, Pack+Encode: 7.753s, Decode+Unpack: 12.815s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5406.2213 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000322-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,236B, BPFP=0.0381 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 61,068B, BPFP=0.0380 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,772B, BPFP=0.0490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,004B, BPFP=0.0485 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 95,016B, BPFP=0.0591 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 95,248B, BPFP=0.0592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,536B, BPFP=0.0588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,220B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 7.820s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 6.11270172 + layer.9.1 0.11112548 6.15429439 + layer.19.0 0.11343976 27.38494906 + layer.19.1 0.08227446 27.07511790 + layer.29.0 0.11178890 277.97956463 + layer.29.1 4.21559211 279.61598615 + layer.39.0 9.18455757 19396.49028972 + layer.39.1 8.88372284 19332.27507163 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 4919.13599690 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 659100 +BPFP 0.0512 bits/point +EBPFP 0.0512 equivalent bits/point +MSE 4919.135997 +---------------------- --------------------------------------------------------- +Time: 22.142s Load: 1.311s, Pack+Encode: 7.820s, Decode+Unpack: 13.012s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4919.1360 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000347-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,188B, BPFP=0.0424 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 66,652B, BPFP=0.0414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 84,764B, BPFP=0.0527 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 83,836B, BPFP=0.0521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,608B, BPFP=0.0644 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,028B, BPFP=0.0641 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,016B, BPFP=0.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,868B, BPFP=0.0596 +⌛️ [2/4] FRONTEND: Frontend time: 7.758s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 6.34686343 + layer.9.1 0.14561824 6.24704824 + layer.19.0 0.12576092 28.08804421 + layer.19.1 0.12606844 28.02634014 + layer.29.0 0.19770402 308.35563913 + layer.29.1 0.18863435 313.08779051 + layer.39.0 84.70259273 21451.75676536 + layer.39.1 43.66404011 20794.29353709 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 5367.02525351 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 703960 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 5367.025254 +---------------------- --------------------------------------------------------- +Time: 21.532s Load: 1.312s, Pack+Encode: 7.758s, Decode+Unpack: 12.462s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5367.0253 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000352-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.288s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,692B, BPFP=0.0396 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,560B, BPFP=0.0395 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,928B, BPFP=0.0516 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 83,496B, BPFP=0.0519 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,748B, BPFP=0.0614 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,972B, BPFP=0.0622 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 90,392B, BPFP=0.0562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 90,136B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 8.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.963s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 6.09654133 + layer.9.1 0.14295322 6.08445447 + layer.19.0 0.05949541 26.29335055 + layer.19.1 0.07012351 26.61232241 + layer.29.0 4.21949463 294.12691022 + layer.29.1 4.23773965 299.42319325 + layer.39.0 8.48589099 15900.12989494 + layer.39.1 10.46205428 15896.12607450 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 4056.86159271 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 672924 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 4056.861593 +---------------------- --------------------------------------------------------- +Time: 22.402s Load: 1.288s, Pack+Encode: 8.150s, Decode+Unpack: 12.963s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4056.8616 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000360-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.314s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,504B, BPFP=0.0382 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,004B, BPFP=0.0386 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,400B, BPFP=0.0506 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,768B, BPFP=0.0515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,104B, BPFP=0.0616 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,532B, BPFP=0.0625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,284B, BPFP=0.0574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 91,184B, BPFP=0.0567 +⌛️ [2/4] FRONTEND: Frontend time: 7.942s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.662s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 6.10821034 + layer.9.1 0.00177230 6.07660528 + layer.19.0 0.01183476 26.21826747 + layer.19.1 0.01005667 26.93930575 + layer.29.0 4.18449569 288.77119150 + layer.29.1 4.18053255 294.83030882 + layer.39.0 7.97218927 16016.79465138 + layer.39.1 7.92115618 15447.34543139 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 4014.13549649 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 670780 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 4014.135496 +---------------------- --------------------------------------------------------- +Time: 21.918s Load: 1.314s, Pack+Encode: 7.942s, Decode+Unpack: 12.662s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4014.1355 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000389-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,160B, BPFP=0.0362 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,708B, BPFP=0.0359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,080B, BPFP=0.0504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,648B, BPFP=0.0501 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,972B, BPFP=0.0628 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,628B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,220B, BPFP=0.0598 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,324B, BPFP=0.0580 +⌛️ [2/4] FRONTEND: Frontend time: 7.720s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.911s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 5.91474650 + layer.9.1 0.03324844 5.94619175 + layer.19.0 0.13337831 27.89206015 + layer.19.1 0.12266011 27.92344198 + layer.29.0 4.22871927 317.08052770 + layer.29.1 4.21185188 313.94941897 + layer.39.0 10.68945623 18521.18688316 + layer.39.1 11.70080065 17960.19866285 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 4647.51149163 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 668740 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 4647.511492 +---------------------- --------------------------------------------------------- +Time: 21.946s Load: 1.316s, Pack+Encode: 7.720s, Decode+Unpack: 12.911s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4647.5115 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000429-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.253s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,216B, BPFP=0.0412 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 66,300B, BPFP=0.0412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 84,972B, BPFP=0.0528 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 84,820B, BPFP=0.0527 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,404B, BPFP=0.0649 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 104,268B, BPFP=0.0648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,672B, BPFP=0.0589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,048B, BPFP=0.0591 +⌛️ [2/4] FRONTEND: Frontend time: 7.707s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.081s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 6.27553489 + layer.9.1 0.14233285 6.28533533 + layer.19.0 0.14139387 29.42087263 + layer.19.1 0.13524239 29.35027161 + layer.29.0 0.16019033 335.95837313 + layer.29.1 0.14649145 329.44738937 + layer.39.0 12.41561455 19470.54950653 + layer.39.1 10.59172910 19617.84145177 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 4978.14109191 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 700700 +BPFP 0.0545 bits/point +EBPFP 0.0545 equivalent bits/point +MSE 4978.141092 +---------------------- --------------------------------------------------------- +Time: 22.041s Load: 1.253s, Pack+Encode: 7.707s, Decode+Unpack: 13.081s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4978.1411 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000436-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.259s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,444B, BPFP=0.0370 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,180B, BPFP=0.0368 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,944B, BPFP=0.0485 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 77,808B, BPFP=0.0484 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,892B, BPFP=0.0602 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 96,772B, BPFP=0.0602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,396B, BPFP=0.0581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,400B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 8.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.754s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 6.07697464 + layer.9.1 0.03247534 6.07626204 + layer.19.0 0.03739121 26.10875567 + layer.19.1 0.03736199 26.10985753 + layer.29.0 4.17784350 276.52417622 + layer.29.1 4.17623735 275.72146609 + layer.39.0 10.57947434 18564.33874562 + layer.39.1 10.58388675 18689.52562878 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 4733.81023332 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 654836 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 4733.810233 +---------------------- --------------------------------------------------------- +Time: 22.221s Load: 1.259s, Pack+Encode: 8.208s, Decode+Unpack: 12.754s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4733.8102 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000442-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.273s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,440B, BPFP=0.0376 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,580B, BPFP=0.0377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,736B, BPFP=0.0490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,188B, BPFP=0.0499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,900B, BPFP=0.0603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,844B, BPFP=0.0615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,824B, BPFP=0.0577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,076B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 7.693s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.477s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 5.91358805 + layer.9.1 0.03247583 5.89876022 + layer.19.0 0.05000294 26.34913990 + layer.19.1 0.04728991 26.53495105 + layer.29.0 4.17616118 277.18728112 + layer.29.1 4.18555745 284.94766794 + layer.39.0 14.92630606 18425.79433301 + layer.39.1 15.22664209 18538.83476600 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 4698.93256091 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 662588 +BPFP 0.0515 bits/point +EBPFP 0.0515 equivalent bits/point +MSE 4698.932561 +---------------------- --------------------------------------------------------- +Time: 21.442s Load: 1.273s, Pack+Encode: 7.693s, Decode+Unpack: 12.477s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4698.9326 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000447-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.279s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,024B, BPFP=0.0392 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,624B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,152B, BPFP=0.0511 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,804B, BPFP=0.0515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,672B, BPFP=0.0620 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,848B, BPFP=0.0621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,540B, BPFP=0.0582 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,612B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 8.275s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.161s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 6.08350931 + layer.9.1 0.11516861 6.11865623 + layer.19.0 0.04822375 27.53338905 + layer.19.1 0.02465675 27.54083343 + layer.29.0 0.12445424 294.77491245 + layer.29.1 4.21809243 301.91955189 + layer.39.0 56.99443848 17649.86692136 + layer.39.1 29.63154648 17726.02355938 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 4504.98266664 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 678276 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 4504.982667 +---------------------- --------------------------------------------------------- +Time: 22.715s Load: 1.279s, Pack+Encode: 8.275s, Decode+Unpack: 13.161s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4504.9827 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000455-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.301s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,412B, BPFP=0.0363 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,916B, BPFP=0.0360 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,908B, BPFP=0.0497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 77,600B, BPFP=0.0483 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,328B, BPFP=0.0643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,660B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,404B, BPFP=0.0618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,772B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 7.827s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.661s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 5.89499948 + layer.9.1 0.14323425 5.97890986 + layer.19.0 0.12097352 26.41956433 + layer.19.1 0.11863553 26.64091850 + layer.29.0 0.18810310 313.93435610 + layer.29.1 0.22084548 311.65946355 + layer.39.0 11.17468934 19309.96115887 + layer.39.1 12.52284677 19769.77141038 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 4971.28259763 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677000 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4971.282598 +---------------------- --------------------------------------------------------- +Time: 21.789s Load: 1.301s, Pack+Encode: 7.827s, Decode+Unpack: 12.661s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4971.2826 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000474-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.341s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,860B, BPFP=0.0372 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,888B, BPFP=0.0372 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,008B, BPFP=0.0504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,948B, BPFP=0.0503 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,648B, BPFP=0.0645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,704B, BPFP=0.0645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 103,280B, BPFP=0.0642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 103,296B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 7.740s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 6.03386660 + layer.9.1 0.14176414 6.02739287 + layer.19.0 0.11837582 26.90364832 + layer.19.1 0.11399856 27.32880154 + layer.29.0 0.14311602 311.08747214 + layer.29.1 0.14520382 314.95292104 + layer.39.0 14.59939236 20804.37185610 + layer.39.1 17.09091825 21138.11142948 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 5329.35217351 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 695632 +BPFP 0.0541 bits/point +EBPFP 0.0541 equivalent bits/point +MSE 5329.352174 +---------------------- --------------------------------------------------------- +Time: 22.056s Load: 1.341s, Pack+Encode: 7.740s, Decode+Unpack: 12.975s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5329.3522 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000476-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.278s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,328B, BPFP=0.0363 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,812B, BPFP=0.0359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,156B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,072B, BPFP=0.0485 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,648B, BPFP=0.0632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,140B, BPFP=0.0623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 100,660B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,496B, BPFP=0.0606 +⌛️ [2/4] FRONTEND: Frontend time: 7.765s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.710s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 5.90867695 + layer.9.1 0.14209374 5.90912217 + layer.19.0 0.05177973 25.69159404 + layer.19.1 0.05586525 26.04211686 + layer.29.0 0.12731753 298.30591372 + layer.29.1 0.12791453 297.21277061 + layer.39.0 10.91882437 19860.28016555 + layer.39.1 9.86751520 19050.38522763 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 4946.21694844 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 673312 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 4946.216948 +---------------------- --------------------------------------------------------- +Time: 21.753s Load: 1.278s, Pack+Encode: 7.765s, Decode+Unpack: 12.710s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4946.2169 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000479-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.278s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,552B, BPFP=0.0408 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 65,608B, BPFP=0.0408 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 84,768B, BPFP=0.0527 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 83,664B, BPFP=0.0520 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,212B, BPFP=0.0648 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,068B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,288B, BPFP=0.0611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 96,456B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 7.641s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.787s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 6.00759922 + layer.9.1 0.03257298 6.02325842 + layer.19.0 0.03929411 26.68009292 + layer.19.1 0.03736255 26.86759541 + layer.29.0 4.19976128 296.96663085 + layer.29.1 4.19887364 295.44723018 + layer.39.0 17.81771704 18659.35561923 + layer.39.1 13.24929237 18902.83985992 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 4777.52348577 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 700616 +BPFP 0.0545 bits/point +EBPFP 0.0545 equivalent bits/point +MSE 4777.523486 +---------------------- --------------------------------------------------------- +Time: 21.706s Load: 1.278s, Pack+Encode: 7.641s, Decode+Unpack: 12.787s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4777.5235 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000489-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.251s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,012B, BPFP=0.0386 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,728B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,480B, BPFP=0.0494 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,648B, BPFP=0.0495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,008B, BPFP=0.0597 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 94,980B, BPFP=0.0591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,644B, BPFP=0.0607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,196B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 8.014s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.699s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 6.13293873 + layer.9.1 0.14206870 6.12988001 + layer.19.0 0.11541664 27.23880233 + layer.19.1 0.11639375 27.90527997 + layer.29.0 4.18928181 280.39545527 + layer.29.1 4.20210771 281.42560092 + layer.39.0 272.14109758 22218.10633556 + layer.39.1 217.56435053 21526.23495702 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 5546.69615623 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 669696 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 5546.696156 +---------------------- --------------------------------------------------------- +Time: 21.965s Load: 1.251s, Pack+Encode: 8.014s, Decode+Unpack: 12.699s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5546.6962 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,904B, BPFP=0.0379 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,208B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,288B, BPFP=0.0499 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,436B, BPFP=0.0500 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,112B, BPFP=0.0629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,164B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,424B, BPFP=0.0600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,428B, BPFP=0.0606 +⌛️ [2/4] FRONTEND: Frontend time: 7.679s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.580s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 6.12756313 + layer.9.1 0.14265629 6.14732507 + layer.19.0 0.15235519 27.82009860 + layer.19.1 0.14002283 27.83311197 + layer.29.0 4.20702410 302.09885387 + layer.29.1 4.22502724 307.40068848 + layer.39.0 9.71896204 19570.77109201 + layer.39.1 14.02077861 20068.92836676 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 5039.64088748 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 678964 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 5039.640887 +---------------------- --------------------------------------------------------- +Time: 21.569s Load: 1.310s, Pack+Encode: 7.679s, Decode+Unpack: 12.580s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5039.6409 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000524-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.262s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,716B, BPFP=0.0353 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 55,492B, BPFP=0.0345 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 75,052B, BPFP=0.0467 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 74,328B, BPFP=0.0462 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 94,004B, BPFP=0.0585 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 91,840B, BPFP=0.0571 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,432B, BPFP=0.0600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,384B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 8.095s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.146s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 5.97755119 + layer.9.1 0.14327397 5.87149482 + layer.19.0 0.03872790 25.61366305 + layer.19.1 0.03991431 24.91967626 + layer.29.0 0.11363128 293.17197947 + layer.29.1 0.09618797 274.35494269 + layer.39.0 113.00349212 23032.92199936 + layer.39.1 66.70960681 22530.11142948 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 5774.11784204 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 638248 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 5774.117842 +---------------------- --------------------------------------------------------- +Time: 22.502s Load: 1.262s, Pack+Encode: 8.095s, Decode+Unpack: 13.146s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5774.1178 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000536-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,708B, BPFP=0.0359 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,788B, BPFP=0.0359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 76,196B, BPFP=0.0474 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 77,232B, BPFP=0.0480 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,224B, BPFP=0.0611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,076B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,852B, BPFP=0.0584 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 91,936B, BPFP=0.0572 +⌛️ [2/4] FRONTEND: Frontend time: 7.808s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.050s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 6.26248794 + layer.9.1 0.14239137 6.19362862 + layer.19.0 0.03888746 28.03485156 + layer.19.1 0.04246985 27.69465586 + layer.29.0 0.10356636 314.05460045 + layer.29.1 0.10009016 308.61164836 + layer.39.0 8.56607607 20338.17255651 + layer.39.1 7.91790657 19765.63769500 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 5099.33276554 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 652012 +BPFP 0.0507 bits/point +EBPFP 0.0507 equivalent bits/point +MSE 5099.332766 +---------------------- --------------------------------------------------------- +Time: 22.172s Load: 1.313s, Pack+Encode: 7.808s, Decode+Unpack: 13.050s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5099.3328 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000546-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.295s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,116B, BPFP=0.0392 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,576B, BPFP=0.0389 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,792B, BPFP=0.0502 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,040B, BPFP=0.0498 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,268B, BPFP=0.0611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,820B, BPFP=0.0614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,512B, BPFP=0.0581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,340B, BPFP=0.0593 +⌛️ [2/4] FRONTEND: Frontend time: 7.888s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.907s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 6.15337099 + layer.9.1 0.14243852 6.19636834 + layer.19.0 0.05701358 27.82034235 + layer.19.1 0.05730241 27.78285429 + layer.29.0 4.14713759 305.73115648 + layer.29.1 4.15440538 303.05547596 + layer.39.0 12.45677755 18078.39159503 + layer.39.1 14.71734096 18597.96879975 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 4669.13749540 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 672464 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 4669.137495 +---------------------- --------------------------------------------------------- +Time: 22.090s Load: 1.295s, Pack+Encode: 7.888s, Decode+Unpack: 12.907s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4669.1375 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000556-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,728B, BPFP=0.0353 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,216B, BPFP=0.0356 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 74,996B, BPFP=0.0466 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 76,044B, BPFP=0.0473 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 95,148B, BPFP=0.0592 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 95,196B, BPFP=0.0592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,492B, BPFP=0.0588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,520B, BPFP=0.0588 +⌛️ [2/4] FRONTEND: Frontend time: 8.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.737s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 6.26047636 + layer.9.1 0.11180697 6.24229383 + layer.19.0 0.09949989 27.56601202 + layer.19.1 0.11883939 27.21480022 + layer.29.0 0.15177689 314.42796880 + layer.29.1 0.14123031 305.00722302 + layer.39.0 349.58010984 21859.29831264 + layer.39.1 334.73010188 21398.24005094 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 5493.03214223 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 644340 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 5493.032142 +---------------------- --------------------------------------------------------- +Time: 22.184s Load: 1.313s, Pack+Encode: 8.134s, Decode+Unpack: 12.737s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5493.0321 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000620-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.275s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,316B, BPFP=0.0394 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,852B, BPFP=0.0397 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,284B, BPFP=0.0505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,676B, BPFP=0.0502 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,888B, BPFP=0.0602 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 95,148B, BPFP=0.0592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 91,208B, BPFP=0.0567 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 89,092B, BPFP=0.0554 +⌛️ [2/4] FRONTEND: Frontend time: 7.680s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.649s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72630507 5.93914284 + layer.9.1 2.71889861 5.90549760 + layer.19.0 3.15508441 25.53772684 + layer.19.1 3.14332772 24.86376005 + layer.29.0 4.15805451 284.40512576 + layer.29.1 4.14588961 278.88677969 + layer.39.0 8.22539970 17434.77873289 + layer.39.1 8.64785859 16662.33174148 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 4340.33106339 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 661464 +BPFP 0.0514 bits/point +EBPFP 0.0514 equivalent bits/point +MSE 4340.331063 +---------------------- --------------------------------------------------------- +Time: 21.604s Load: 1.275s, Pack+Encode: 7.680s, Decode+Unpack: 12.649s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4340.3311 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000624-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.286s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,268B, BPFP=0.0393 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,236B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,132B, BPFP=0.0504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,944B, BPFP=0.0491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,240B, BPFP=0.0630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 96,364B, BPFP=0.0599 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,056B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,892B, BPFP=0.0596 +⌛️ [2/4] FRONTEND: Frontend time: 7.869s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.687s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 6.07054444 + layer.9.1 0.11119189 6.13832428 + layer.19.0 0.08174444 26.29662627 + layer.19.1 0.08249469 26.77554173 + layer.29.0 4.18188438 288.15773241 + layer.29.1 4.20908200 284.42578001 + layer.39.0 9.33443395 19453.69627507 + layer.39.1 9.53268950 19743.54791468 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 4979.38859236 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 678132 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 4979.388592 +---------------------- --------------------------------------------------------- +Time: 21.842s Load: 1.286s, Pack+Encode: 7.869s, Decode+Unpack: 12.687s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4979.3886 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.283s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,964B, BPFP=0.0379 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 61,968B, BPFP=0.0385 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,200B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,468B, BPFP=0.0494 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,452B, BPFP=0.0618 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,320B, BPFP=0.0618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,004B, BPFP=0.0591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,020B, BPFP=0.0591 +⌛️ [2/4] FRONTEND: Frontend time: 7.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.382s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 6.14448772 + layer.9.1 0.03285184 6.22869782 + layer.19.0 0.04037820 26.79169154 + layer.19.1 0.04362713 27.02471595 + layer.29.0 0.11518513 312.54375597 + layer.29.1 0.11703357 308.82129497 + layer.39.0 256.78569723 19588.45208532 + layer.39.1 143.16752229 18862.40305635 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 4892.30122321 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 670396 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 4892.301223 +---------------------- --------------------------------------------------------- +Time: 21.183s Load: 1.283s, Pack+Encode: 7.518s, Decode+Unpack: 12.382s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4892.3012 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000676-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,500B, BPFP=0.0376 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,660B, BPFP=0.0377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,416B, BPFP=0.0506 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,928B, BPFP=0.0503 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,524B, BPFP=0.0631 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,924B, BPFP=0.0621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,820B, BPFP=0.0614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 96,288B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 7.771s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.974s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 6.26423835 + layer.9.1 0.11256296 6.26119083 + layer.19.0 0.03396921 27.17856077 + layer.19.1 0.04105656 27.34495632 + layer.29.0 4.20373127 290.26016794 + layer.29.1 4.19418701 292.91835801 + layer.39.0 8.83613586 18552.42024833 + layer.39.1 8.48765384 18231.43966890 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 4679.26092368 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 680060 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 4679.260924 +---------------------- --------------------------------------------------------- +Time: 22.055s Load: 1.311s, Pack+Encode: 7.771s, Decode+Unpack: 12.974s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4679.2609 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000678-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,592B, BPFP=0.0371 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,032B, BPFP=0.0373 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,172B, BPFP=0.0480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,740B, BPFP=0.0490 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,320B, BPFP=0.0611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,320B, BPFP=0.0624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,344B, BPFP=0.0599 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,908B, BPFP=0.0609 +⌛️ [2/4] FRONTEND: Frontend time: 7.665s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 6.05095909 + layer.9.1 0.03228644 6.01878258 + layer.19.0 0.12067159 26.27041796 + layer.19.1 0.11791951 26.52247742 + layer.29.0 0.15835167 304.79391515 + layer.29.1 0.15268422 312.87961636 + layer.39.0 158.29335801 20414.35721108 + layer.39.1 131.92238738 20873.64024196 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 5246.31670270 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 668428 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 5246.316703 +---------------------- --------------------------------------------------------- +Time: 21.921s Load: 1.242s, Pack+Encode: 7.665s, Decode+Unpack: 13.014s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5246.3167 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000684-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,448B, BPFP=0.0363 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,108B, BPFP=0.0361 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,420B, BPFP=0.0494 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,816B, BPFP=0.0490 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,444B, BPFP=0.0600 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 97,900B, BPFP=0.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,112B, BPFP=0.0579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,484B, BPFP=0.0588 +⌛️ [2/4] FRONTEND: Frontend time: 7.919s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.726s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 6.00698922 + layer.9.1 0.03230341 6.00284419 + layer.19.0 0.01113602 26.76697310 + layer.19.1 0.03747142 26.71568320 + layer.29.0 4.12172023 285.78390640 + layer.29.1 4.13913264 290.28892073 + layer.39.0 9.31610902 17150.82203120 + layer.39.1 11.00762596 17559.71092009 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 4419.01228351 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 656732 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 4419.012284 +---------------------- --------------------------------------------------------- +Time: 21.889s Load: 1.244s, Pack+Encode: 7.919s, Decode+Unpack: 12.726s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4419.0123 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000693-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.279s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,896B, BPFP=0.0366 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,428B, BPFP=0.0370 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,048B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,220B, BPFP=0.0493 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,728B, BPFP=0.0626 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,848B, BPFP=0.0633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 103,336B, BPFP=0.0643 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 103,992B, BPFP=0.0647 +⌛️ [2/4] FRONTEND: Frontend time: 7.588s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.871s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14187056 6.00108569 + layer.9.1 0.14241365 6.01070892 + layer.19.0 0.11657135 27.74357788 + layer.19.1 0.11473399 27.09511800 + layer.29.0 0.16421308 308.18445559 + layer.29.1 0.18111406 308.77322111 + layer.39.0 55.30549089 21238.03756765 + layer.39.1 49.87731316 21104.21267112 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 5378.25730075 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 686496 +BPFP 0.0534 bits/point +EBPFP 0.0534 equivalent bits/point +MSE 5378.257301 +---------------------- --------------------------------------------------------- +Time: 21.737s Load: 1.279s, Pack+Encode: 7.588s, Decode+Unpack: 12.871s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5378.2573 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000713-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.315s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,032B, BPFP=0.0386 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,844B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,100B, BPFP=0.0504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,380B, BPFP=0.0506 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,408B, BPFP=0.0624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,088B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,096B, BPFP=0.0585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,872B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 7.631s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 5.94348685 + layer.9.1 0.03232725 5.91008598 + layer.19.0 0.03714494 24.91245075 + layer.19.1 0.03685654 24.90993364 + layer.29.0 4.16145554 289.56423114 + layer.29.1 4.17130075 295.74128462 + layer.39.0 7.63807493 18011.26010825 + layer.39.1 7.26751532 17857.82744349 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 4564.50862809 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 676820 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4564.508628 +---------------------- --------------------------------------------------------- +Time: 21.396s Load: 1.315s, Pack+Encode: 7.631s, Decode+Unpack: 12.450s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4564.5086 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000734-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,404B, BPFP=0.0388 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,860B, BPFP=0.0391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,112B, BPFP=0.0504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,508B, BPFP=0.0507 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,864B, BPFP=0.0621 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,828B, BPFP=0.0621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,208B, BPFP=0.0592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,372B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 7.898s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.692s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 6.03001569 + layer.9.1 0.14394252 6.10192253 + layer.19.0 0.03713998 25.58789498 + layer.19.1 0.11359857 26.57777678 + layer.29.0 4.20669858 283.42655603 + layer.29.1 0.11083615 290.91370185 + layer.39.0 7.41086201 16603.33014963 + layer.39.1 8.74303628 16213.70646291 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 4181.95931005 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677156 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4181.959310 +---------------------- --------------------------------------------------------- +Time: 21.745s Load: 1.155s, Pack+Encode: 7.898s, Decode+Unpack: 12.692s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4181.9593 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000737-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.131s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,024B, BPFP=0.0355 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,292B, BPFP=0.0356 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,968B, BPFP=0.0497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,476B, BPFP=0.0500 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,196B, BPFP=0.0623 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,116B, BPFP=0.0623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,516B, BPFP=0.0575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,176B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 7.798s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 5.96787946 + layer.9.1 0.14198353 6.06035722 + layer.19.0 0.17418623 27.94925730 + layer.19.1 0.18921874 28.44548661 + layer.29.0 0.15243895 336.68035657 + layer.29.1 0.17994503 343.13650111 + layer.39.0 13.57905399 18017.76122254 + layer.39.1 8.80701993 18639.11875199 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 4675.63997660 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 660764 +BPFP 0.0514 bits/point +EBPFP 0.0514 equivalent bits/point +MSE 4675.639977 +---------------------- --------------------------------------------------------- +Time: 21.934s Load: 1.131s, Pack+Encode: 7.798s, Decode+Unpack: 13.005s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4675.6400 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000804-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.121s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,232B, BPFP=0.0368 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,180B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,052B, BPFP=0.0510 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,336B, BPFP=0.0506 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,320B, BPFP=0.0624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,156B, BPFP=0.0623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,100B, BPFP=0.0573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,136B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 8.035s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.750s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72336357 6.06098339 + layer.9.1 2.61637510 6.02105843 + layer.19.0 0.14860626 28.82339422 + layer.19.1 0.15499876 27.54258695 + layer.29.0 0.29089499 337.41328399 + layer.29.1 0.20993857 324.91865648 + layer.39.0 12.63850088 17651.72238141 + layer.39.1 9.97545753 17343.01687361 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 4465.68990231 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 665512 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 4465.689902 +---------------------- --------------------------------------------------------- +Time: 21.906s Load: 1.121s, Pack+Encode: 8.035s, Decode+Unpack: 12.750s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4465.6899 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000816-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,892B, BPFP=0.0354 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,184B, BPFP=0.0356 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,120B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,464B, BPFP=0.0494 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,532B, BPFP=0.0613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,928B, BPFP=0.0615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 90,404B, BPFP=0.0562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 90,584B, BPFP=0.0563 +⌛️ [2/4] FRONTEND: Frontend time: 8.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.157s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 6.02900027 + layer.9.1 0.14187655 6.02401703 + layer.19.0 0.17405892 27.95478649 + layer.19.1 0.14315577 28.40341949 + layer.29.0 0.19218995 330.62718879 + layer.29.1 0.16272765 317.21255174 + layer.39.0 14.01399584 18054.91244826 + layer.39.1 9.48776763 17999.00668577 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 4596.27126223 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 651108 +BPFP 0.0506 bits/point +EBPFP 0.0506 equivalent bits/point +MSE 4596.271262 +---------------------- --------------------------------------------------------- +Time: 22.441s Load: 1.149s, Pack+Encode: 8.135s, Decode+Unpack: 13.157s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4596.2713 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000817-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.148s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,824B, BPFP=0.0397 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 65,100B, BPFP=0.0405 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,580B, BPFP=0.0507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 83,368B, BPFP=0.0518 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,576B, BPFP=0.0613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,732B, BPFP=0.0620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,352B, BPFP=0.0612 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,272B, BPFP=0.0611 +⌛️ [2/4] FRONTEND: Frontend time: 7.907s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.810s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 6.18348431 + layer.9.1 0.14252999 6.10512551 + layer.19.0 0.12443910 29.00222610 + layer.19.1 0.13256963 28.43440833 + layer.29.0 4.20758094 295.99458771 + layer.29.1 4.18155761 292.52248488 + layer.39.0 45.67507362 21217.93696275 + layer.39.1 52.99942295 20799.57083731 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 5334.46876461 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 688804 +BPFP 0.0535 bits/point +EBPFP 0.0535 equivalent bits/point +MSE 5334.468765 +---------------------- --------------------------------------------------------- +Time: 21.866s Load: 1.148s, Pack+Encode: 7.907s, Decode+Unpack: 12.810s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5334.4688 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000880-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.144s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,276B, BPFP=0.0362 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,156B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,052B, BPFP=0.0498 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,912B, BPFP=0.0497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,152B, BPFP=0.0623 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,636B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 90,152B, BPFP=0.0561 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,120B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 7.999s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.102s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 5.98308038 + layer.9.1 0.14194541 6.02331562 + layer.19.0 0.11782019 28.49309038 + layer.19.1 0.12099331 28.69125577 + layer.29.0 0.31534543 349.74375199 + layer.29.1 0.31351768 350.29381566 + layer.39.0 16.41217467 17617.87583572 + layer.39.1 11.15875965 17987.68545049 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 4546.84869950 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 659456 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 4546.848700 +---------------------- --------------------------------------------------------- +Time: 22.245s Load: 1.144s, Pack+Encode: 7.999s, Decode+Unpack: 13.102s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4546.8487 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,288B, BPFP=0.0406 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 66,124B, BPFP=0.0411 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,808B, BPFP=0.0515 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 83,204B, BPFP=0.0517 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,340B, BPFP=0.0636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,296B, BPFP=0.0636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,612B, BPFP=0.0595 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,472B, BPFP=0.0594 +⌛️ [2/4] FRONTEND: Frontend time: 7.850s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.645s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 6.04360300 + layer.9.1 0.14279503 6.03826844 + layer.19.0 0.04409784 27.12585811 + layer.19.1 0.12204415 27.16038632 + layer.29.0 0.14332971 320.74705508 + layer.29.1 0.16018698 321.05475963 + layer.39.0 8.52841700 18980.81884750 + layer.39.1 19.04729908 18499.59248647 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 4773.57265807 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 693144 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 4773.572658 +---------------------- --------------------------------------------------------- +Time: 21.642s Load: 1.147s, Pack+Encode: 7.850s, Decode+Unpack: 12.645s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4773.5727 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000892-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.167s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,120B, BPFP=0.0368 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,276B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,568B, BPFP=0.0489 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,708B, BPFP=0.0489 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,392B, BPFP=0.0612 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,936B, BPFP=0.0615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 91,820B, BPFP=0.0571 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,628B, BPFP=0.0576 +⌛️ [2/4] FRONTEND: Frontend time: 7.754s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.437s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 6.14900149 + layer.9.1 0.03263012 6.08310637 + layer.19.0 0.05225635 28.61069823 + layer.19.1 0.04916960 27.89115230 + layer.29.0 4.19413323 322.46780484 + layer.29.1 4.20728930 318.55155603 + layer.39.0 8.98594322 19261.31932506 + layer.39.1 8.30659896 19338.22094874 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 4913.66169913 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 656448 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 4913.661699 +---------------------- --------------------------------------------------------- +Time: 21.357s Load: 1.167s, Pack+Encode: 7.754s, Decode+Unpack: 12.437s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4913.6617 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000919-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,208B, BPFP=0.0387 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,568B, BPFP=0.0389 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,880B, BPFP=0.0497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,600B, BPFP=0.0495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,512B, BPFP=0.0625 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,520B, BPFP=0.0625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,580B, BPFP=0.0613 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,424B, BPFP=0.0612 +⌛️ [2/4] FRONTEND: Frontend time: 7.914s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.861s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258133 6.08638086 + layer.9.1 0.03283905 6.05938408 + layer.19.0 0.03703246 26.38625736 + layer.19.1 0.03684524 25.70380402 + layer.29.0 0.11326863 307.97697787 + layer.29.1 0.10834243 302.63689908 + layer.39.0 11.60468402 19951.72620185 + layer.39.1 14.87000682 20170.10124164 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 5099.58464334 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 682292 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 5099.584643 +---------------------- --------------------------------------------------------- +Time: 21.957s Load: 1.183s, Pack+Encode: 7.914s, Decode+Unpack: 12.861s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5099.5846 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000925-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,452B, BPFP=0.0370 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,604B, BPFP=0.0371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,148B, BPFP=0.0511 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,120B, BPFP=0.0511 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,280B, BPFP=0.0636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,356B, BPFP=0.0636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 102,716B, BPFP=0.0639 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 102,488B, BPFP=0.0637 +⌛️ [2/4] FRONTEND: Frontend time: 7.832s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.954s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 6.08965969 + layer.9.1 0.11188250 6.10964984 + layer.19.0 3.25906142 26.72636849 + layer.19.1 3.26015426 26.81339293 + layer.29.0 4.19564952 313.02336039 + layer.29.1 4.21244012 314.35872334 + layer.39.0 303.99934336 22703.25119389 + layer.39.1 331.94728988 22309.60331105 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 5713.24695745 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 693164 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 5713.246957 +---------------------- --------------------------------------------------------- +Time: 22.103s Load: 1.317s, Pack+Encode: 7.832s, Decode+Unpack: 12.954s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5713.2470 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000927-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.184s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,528B, BPFP=0.0401 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,740B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 84,620B, BPFP=0.0526 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 84,220B, BPFP=0.0524 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,188B, BPFP=0.0648 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,072B, BPFP=0.0653 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 101,896B, BPFP=0.0634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 103,520B, BPFP=0.0644 +⌛️ [2/4] FRONTEND: Frontend time: 7.901s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 5.98734915 + layer.9.1 0.00271392 5.98397579 + layer.19.0 3.19073251 26.91903952 + layer.19.1 3.15044721 26.23996140 + layer.29.0 4.17151372 292.22675899 + layer.29.1 4.17302847 293.37193569 + layer.39.0 85.12206503 23383.93632601 + layer.39.1 85.43754975 23341.65170328 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 5922.03963123 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 711784 +BPFP 0.0553 bits/point +EBPFP 0.0553 equivalent bits/point +MSE 5922.039631 +---------------------- --------------------------------------------------------- +Time: 22.091s Load: 1.184s, Pack+Encode: 7.901s, Decode+Unpack: 13.005s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5922.0396 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000942-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.330s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,028B, BPFP=0.0355 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,568B, BPFP=0.0364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,112B, BPFP=0.0486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,232B, BPFP=0.0493 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 97,636B, BPFP=0.0607 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,588B, BPFP=0.0613 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,548B, BPFP=0.0588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,300B, BPFP=0.0586 +⌛️ [2/4] FRONTEND: Frontend time: 8.011s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.052s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 6.02750604 + layer.9.1 2.75948239 6.04914836 + layer.19.0 0.15224024 27.61689400 + layer.19.1 0.13045117 27.58812629 + layer.29.0 0.13097460 310.15335482 + layer.29.1 0.13177276 307.80014327 + layer.39.0 10.49186664 19588.78701051 + layer.39.1 12.55703299 18619.14549507 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 4861.64595980 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 658012 +BPFP 0.0511 bits/point +EBPFP 0.0511 equivalent bits/point +MSE 4861.645960 +---------------------- --------------------------------------------------------- +Time: 22.392s Load: 1.330s, Pack+Encode: 8.011s, Decode+Unpack: 13.052s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4861.6460 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000946-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,304B, BPFP=0.0375 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,220B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,264B, BPFP=0.0512 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,604B, BPFP=0.0507 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,752B, BPFP=0.0645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,596B, BPFP=0.0638 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,636B, BPFP=0.0613 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,848B, BPFP=0.0615 +⌛️ [2/4] FRONTEND: Frontend time: 7.768s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.692s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 5.97130691 + layer.9.1 0.03228249 5.97647545 + layer.19.0 0.04154089 26.57360564 + layer.19.1 0.04120101 26.67922238 + layer.29.0 4.21417063 300.30362544 + layer.29.1 4.21428318 297.71497931 + layer.39.0 28.58093312 18439.64342566 + layer.39.1 17.10356972 18351.58739255 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 4681.80625417 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 688224 +BPFP 0.0535 bits/point +EBPFP 0.0535 equivalent bits/point +MSE 4681.806254 +---------------------- --------------------------------------------------------- +Time: 21.773s Load: 1.313s, Pack+Encode: 7.768s, Decode+Unpack: 12.692s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4681.8063 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000959-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,444B, BPFP=0.0370 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,352B, BPFP=0.0369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,240B, BPFP=0.0493 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,216B, BPFP=0.0493 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,580B, BPFP=0.0619 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,552B, BPFP=0.0619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,596B, BPFP=0.0601 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 96,632B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 7.864s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.738s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 6.04996418 + layer.9.1 0.14242138 6.04633402 + layer.19.0 0.13512425 27.98142262 + layer.19.1 0.13152432 27.90954314 + layer.29.0 0.11439834 297.39941500 + layer.29.1 0.11806111 296.53257323 + layer.39.0 18.41482236 19965.97007323 + layer.39.1 20.38586935 19960.67749124 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 5073.57085208 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 669612 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 5073.570852 +---------------------- --------------------------------------------------------- +Time: 21.905s Load: 1.304s, Pack+Encode: 7.864s, Decode+Unpack: 12.738s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5073.5709 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000972-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,856B, BPFP=0.0410 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 67,064B, BPFP=0.0417 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 83,056B, BPFP=0.0516 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,932B, BPFP=0.0516 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,600B, BPFP=0.0619 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,552B, BPFP=0.0625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,588B, BPFP=0.0576 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,064B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 7.808s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.605s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 6.00470528 + layer.9.1 0.14251336 6.00959587 + layer.19.0 0.11881898 27.29206463 + layer.19.1 0.11371834 26.34734410 + layer.29.0 0.15377442 304.14955428 + layer.29.1 0.16319071 307.71601401 + layer.39.0 9.10150218 18018.12416428 + layer.39.1 9.15265777 18192.47755492 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 4611.01512467 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 685712 +BPFP 0.0533 bits/point +EBPFP 0.0533 equivalent bits/point +MSE 4611.015125 +---------------------- --------------------------------------------------------- +Time: 21.718s Load: 1.305s, Pack+Encode: 7.808s, Decode+Unpack: 12.605s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4611.0151 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001000-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,496B, BPFP=0.0376 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,312B, BPFP=0.0375 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,980B, BPFP=0.0504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,116B, BPFP=0.0504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,332B, BPFP=0.0611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,064B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,520B, BPFP=0.0575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,640B, BPFP=0.0576 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 5.92799243 + layer.9.1 0.14223260 5.91372485 + layer.19.0 0.05715554 27.30507551 + layer.19.1 0.06015340 27.62735544 + layer.29.0 0.19165729 318.35006765 + layer.29.1 0.21090307 321.56144540 + layer.39.0 19.07211701 18905.65679720 + layer.39.1 16.66110887 18902.21076090 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 4814.31915242 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 665460 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 4814.319152 +---------------------- --------------------------------------------------------- +Time: 22.035s Load: 1.163s, Pack+Encode: 7.878s, Decode+Unpack: 12.994s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4814.3192 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,632B, BPFP=0.0352 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,192B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,036B, BPFP=0.0479 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,108B, BPFP=0.0492 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,676B, BPFP=0.0601 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,832B, BPFP=0.0615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,672B, BPFP=0.0607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 100,332B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 7.775s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.783s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 5.97192562 + layer.9.1 0.14288678 5.95404280 + layer.19.0 0.11144568 27.22683361 + layer.19.1 0.11742487 26.92366086 + layer.29.0 0.11418290 294.78844317 + layer.29.1 0.10734091 293.91027937 + layer.39.0 54.48020137 21779.21935689 + layer.39.1 66.40954314 22136.86341929 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 5571.35724520 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 664480 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 5571.357245 +---------------------- --------------------------------------------------------- +Time: 21.870s Load: 1.312s, Pack+Encode: 7.775s, Decode+Unpack: 12.783s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5571.3572 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001056-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.286s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,932B, BPFP=0.0360 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,568B, BPFP=0.0364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,724B, BPFP=0.0483 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,820B, BPFP=0.0490 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,204B, BPFP=0.0611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,036B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,328B, BPFP=0.0587 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,340B, BPFP=0.0593 +⌛️ [2/4] FRONTEND: Frontend time: 8.037s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.797s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 5.94752554 + layer.9.1 0.00081411 5.92650691 + layer.19.0 0.01015774 26.29157713 + layer.19.1 3.16362350 26.02137058 + layer.29.0 4.19769406 299.78744826 + layer.29.1 4.18061463 295.66396052 + layer.39.0 8.41366640 18976.28525947 + layer.39.1 8.38033145 18859.15950334 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 4811.88539397 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 659952 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 4811.885394 +---------------------- --------------------------------------------------------- +Time: 22.120s Load: 1.286s, Pack+Encode: 8.037s, Decode+Unpack: 12.797s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4811.8854 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001074-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.315s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,552B, BPFP=0.0352 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,332B, BPFP=0.0356 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,980B, BPFP=0.0491 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,196B, BPFP=0.0492 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,232B, BPFP=0.0629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,864B, BPFP=0.0640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,420B, BPFP=0.0600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,088B, BPFP=0.0616 +⌛️ [2/4] FRONTEND: Frontend time: 7.702s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.873s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 5.78450272 + layer.9.1 0.03271215 5.79723690 + layer.19.0 3.19210144 25.23540722 + layer.19.1 3.19171965 25.31075394 + layer.29.0 0.11530653 307.53108087 + layer.29.1 0.10966549 317.50310411 + layer.39.0 16.12381606 19775.38618274 + layer.39.1 25.33235335 20808.96147724 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 5158.93871822 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 671664 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 5158.938718 +---------------------- --------------------------------------------------------- +Time: 21.890s Load: 1.315s, Pack+Encode: 7.702s, Decode+Unpack: 12.873s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5158.9387 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001078-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,660B, BPFP=0.0352 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 55,540B, BPFP=0.0345 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,132B, BPFP=0.0480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 76,548B, BPFP=0.0476 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,368B, BPFP=0.0612 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,720B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,976B, BPFP=0.0591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 100,160B, BPFP=0.0623 +⌛️ [2/4] FRONTEND: Frontend time: 8.240s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.764s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64207787 5.75674609 + layer.9.1 0.03100527 5.76553484 + layer.19.0 3.19321449 25.38233495 + layer.19.1 3.20089330 25.91713925 + layer.29.0 0.10652387 292.34982888 + layer.29.1 0.17364564 322.24017033 + layer.39.0 9.89558772 17566.51384909 + layer.39.1 12.87769495 19476.76408787 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 4715.08621141 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 660104 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 4715.086211 +---------------------- --------------------------------------------------------- +Time: 22.313s Load: 1.309s, Pack+Encode: 8.240s, Decode+Unpack: 12.764s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4715.0862 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001086-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,904B, BPFP=0.0372 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,644B, BPFP=0.0371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,108B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,616B, BPFP=0.0489 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,956B, BPFP=0.0622 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,140B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,804B, BPFP=0.0614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 101,872B, BPFP=0.0633 +⌛️ [2/4] FRONTEND: Frontend time: 7.764s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 5.83306968 + layer.9.1 0.03183258 5.84325379 + layer.19.0 0.03873757 25.59428725 + layer.19.1 0.03841183 25.32251622 + layer.29.0 0.10242378 294.89044094 + layer.29.1 0.10979955 302.47695798 + layer.39.0 11.55027136 20841.95606495 + layer.39.1 12.74680635 20914.49856734 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 5302.05189477 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 680044 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 5302.051895 +---------------------- --------------------------------------------------------- +Time: 21.675s Load: 1.309s, Pack+Encode: 7.764s, Decode+Unpack: 12.601s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5302.0519 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001102-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,820B, BPFP=0.0347 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 55,744B, BPFP=0.0347 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 73,924B, BPFP=0.0460 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 74,224B, BPFP=0.0462 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 95,148B, BPFP=0.0592 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 95,532B, BPFP=0.0594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,212B, BPFP=0.0611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,668B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 7.715s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.880s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 5.99884591 + layer.9.1 0.03112686 6.01261790 + layer.19.0 0.03695946 25.42053685 + layer.19.1 0.03932408 25.67711816 + layer.29.0 0.11080087 279.23483763 + layer.29.1 0.12351766 284.38128781 + layer.39.0 27.63217079 20303.45113021 + layer.39.1 35.42625259 20070.99649793 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 5125.14660905 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 647272 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 5125.146609 +---------------------- --------------------------------------------------------- +Time: 21.814s Load: 1.220s, Pack+Encode: 7.715s, Decode+Unpack: 12.880s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5125.1466 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001107-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.293s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,684B, BPFP=0.0384 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 61,832B, BPFP=0.0384 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,996B, BPFP=0.0491 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,308B, BPFP=0.0487 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,780B, BPFP=0.0620 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,344B, BPFP=0.0624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,904B, BPFP=0.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,092B, BPFP=0.0610 +⌛️ [2/4] FRONTEND: Frontend time: 8.356s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.102s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 6.06281713 + layer.9.1 0.11126176 6.10146052 + layer.19.0 0.00622823 25.93917642 + layer.19.1 0.00986777 25.74613479 + layer.29.0 4.20227933 299.65162369 + layer.29.1 4.19170939 306.06421124 + layer.39.0 64.89367936 19027.64469914 + layer.39.1 48.85537050 18771.82553327 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 4808.62945702 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 676940 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4808.629457 +---------------------- --------------------------------------------------------- +Time: 22.751s Load: 1.293s, Pack+Encode: 8.356s, Decode+Unpack: 13.102s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4808.6295 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001116-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,852B, BPFP=0.0360 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,528B, BPFP=0.0358 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,184B, BPFP=0.0505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,012B, BPFP=0.0504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 105,696B, BPFP=0.0657 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,232B, BPFP=0.0654 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,460B, BPFP=0.0618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,816B, BPFP=0.0621 +⌛️ [2/4] FRONTEND: Frontend time: 7.896s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.683s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 5.93036590 + layer.9.1 0.03110840 5.93003136 + layer.19.0 0.11193399 26.47205060 + layer.19.1 0.11167925 26.56696215 + layer.29.0 0.13638519 319.04387536 + layer.29.1 0.13233996 310.19124085 + layer.39.0 10.36537055 18160.98949379 + layer.39.1 10.25938570 17826.27952881 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 4585.17544360 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 687780 +BPFP 0.0535 bits/point +EBPFP 0.0535 equivalent bits/point +MSE 4585.175444 +---------------------- --------------------------------------------------------- +Time: 21.886s Load: 1.306s, Pack+Encode: 7.896s, Decode+Unpack: 12.683s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4585.1754 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001125-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.282s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,656B, BPFP=0.0365 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,440B, BPFP=0.0363 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,480B, BPFP=0.0494 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,632B, BPFP=0.0489 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,732B, BPFP=0.0639 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,364B, BPFP=0.0630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,636B, BPFP=0.0620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,268B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 7.939s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.836s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14239891 5.95917030 + layer.9.1 0.14185137 5.93430013 + layer.19.0 0.03937967 26.67979943 + layer.19.1 0.04081462 26.72180934 + layer.29.0 4.18784542 295.59604823 + layer.29.1 4.19318340 299.69251433 + layer.39.0 9.46241929 18657.99681630 + layer.39.1 9.25020271 19016.58325374 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 4791.89546397 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 676208 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4791.895464 +---------------------- --------------------------------------------------------- +Time: 22.058s Load: 1.282s, Pack+Encode: 7.939s, Decode+Unpack: 12.836s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4791.8955 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001139-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,992B, BPFP=0.0361 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,864B, BPFP=0.0360 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,524B, BPFP=0.0494 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,468B, BPFP=0.0494 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,712B, BPFP=0.0632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,120B, BPFP=0.0641 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,376B, BPFP=0.0612 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,780B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 7.820s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.581s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 5.94890909 + layer.9.1 0.14180939 5.92885427 + layer.19.0 0.04123239 26.16082159 + layer.19.1 0.03889530 26.21005452 + layer.29.0 0.17016378 306.72029210 + layer.29.1 0.15026704 310.06128621 + layer.39.0 12.11620503 18408.95510984 + layer.39.1 10.53236554 18446.88697867 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 4692.10903829 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677836 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 4692.109038 +---------------------- --------------------------------------------------------- +Time: 21.678s Load: 1.276s, Pack+Encode: 7.820s, Decode+Unpack: 12.581s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4692.1090 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001145-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.258s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,640B, BPFP=0.0365 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,212B, BPFP=0.0362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,120B, BPFP=0.0498 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,564B, BPFP=0.0489 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,356B, BPFP=0.0630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,844B, BPFP=0.0621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,976B, BPFP=0.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,760B, BPFP=0.0595 +⌛️ [2/4] FRONTEND: Frontend time: 7.856s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.706s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 6.07904840 + layer.9.1 0.11141965 6.03419554 + layer.19.0 0.02960617 26.65965009 + layer.19.1 0.09893673 26.30576697 + layer.29.0 0.11288278 297.81520614 + layer.29.1 0.12156463 301.55493871 + layer.39.0 13.31952528 18480.52594715 + layer.39.1 8.92088009 18244.60235594 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 4673.69713862 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 670472 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 4673.697139 +---------------------- --------------------------------------------------------- +Time: 21.820s Load: 1.258s, Pack+Encode: 7.856s, Decode+Unpack: 12.706s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4673.6971 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001171-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,800B, BPFP=0.0353 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 56,444B, BPFP=0.0351 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,952B, BPFP=0.0497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,828B, BPFP=0.0490 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,452B, BPFP=0.0643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,332B, BPFP=0.0636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 101,860B, BPFP=0.0633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 101,224B, BPFP=0.0629 +⌛️ [2/4] FRONTEND: Frontend time: 7.831s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.898s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 5.90096643 + layer.9.1 0.03269095 5.88551802 + layer.19.0 0.03939078 26.66532852 + layer.19.1 0.03751187 26.43694783 + layer.29.0 0.14354374 318.52652420 + layer.29.1 0.12315212 308.35444524 + layer.39.0 10.67588198 19045.14103789 + layer.39.1 12.04857131 18541.42884432 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 4784.79245155 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 680892 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 4784.792452 +---------------------- --------------------------------------------------------- +Time: 21.968s Load: 1.239s, Pack+Encode: 7.831s, Decode+Unpack: 12.898s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4784.7925 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001179-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.285s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,584B, BPFP=0.0364 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,740B, BPFP=0.0365 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,136B, BPFP=0.0492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,964B, BPFP=0.0497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,904B, BPFP=0.0634 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,532B, BPFP=0.0638 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,936B, BPFP=0.0603 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,320B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 7.925s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 5.94934871 + layer.9.1 0.03246013 5.93488153 + layer.19.0 0.05054442 27.35052034 + layer.19.1 0.04990058 27.20629875 + layer.29.0 4.26185866 328.67076568 + layer.29.1 4.26378007 328.21422318 + layer.39.0 11.04594849 19788.25851640 + layer.39.1 9.19037403 19913.66825852 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 5053.15660164 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 675116 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 5053.156602 +---------------------- --------------------------------------------------------- +Time: 21.723s Load: 1.285s, Pack+Encode: 7.925s, Decode+Unpack: 12.512s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5053.1566 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001184-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.314s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,852B, BPFP=0.0354 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 56,856B, BPFP=0.0354 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 76,240B, BPFP=0.0474 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 76,640B, BPFP=0.0477 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 94,328B, BPFP=0.0587 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 96,456B, BPFP=0.0600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,432B, BPFP=0.0575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,772B, BPFP=0.0596 +⌛️ [2/4] FRONTEND: Frontend time: 7.790s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.729s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 5.98144500 + layer.9.1 0.14317998 5.96239379 + layer.19.0 0.15093802 27.27704403 + layer.19.1 0.13472426 26.69539209 + layer.29.0 0.10723148 297.16780086 + layer.29.1 0.10832139 298.34222779 + layer.39.0 40.62415433 18577.07736390 + layer.39.1 9.85226018 18867.18369946 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 4763.21092087 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 645576 +BPFP 0.0502 bits/point +EBPFP 0.0502 equivalent bits/point +MSE 4763.210921 +---------------------- --------------------------------------------------------- +Time: 21.832s Load: 1.314s, Pack+Encode: 7.790s, Decode+Unpack: 12.729s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4763.2109 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001198-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,480B, BPFP=0.0357 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,884B, BPFP=0.0360 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,488B, BPFP=0.0507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,772B, BPFP=0.0502 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,964B, BPFP=0.0634 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,032B, BPFP=0.0634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,572B, BPFP=0.0607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,128B, BPFP=0.0610 +⌛️ [2/4] FRONTEND: Frontend time: 7.748s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.678s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 5.88282680 + layer.9.1 0.03106517 5.89139792 + layer.19.0 0.04795660 27.55236937 + layer.19.1 0.11462555 27.36637168 + layer.29.0 4.19919699 313.41638809 + layer.29.1 4.19569772 311.57300621 + layer.39.0 34.63583701 19713.81852913 + layer.39.1 33.06685271 20207.28812480 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 5076.59862675 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677320 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 5076.598627 +---------------------- --------------------------------------------------------- +Time: 21.715s Load: 1.289s, Pack+Encode: 7.748s, Decode+Unpack: 12.678s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5076.5986 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001272-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.286s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,800B, BPFP=0.0384 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 61,040B, BPFP=0.0380 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,152B, BPFP=0.0480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 76,520B, BPFP=0.0476 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,716B, BPFP=0.0601 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 97,572B, BPFP=0.0607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,468B, BPFP=0.0587 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 96,380B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 7.918s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.661s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 6.07928407 + layer.9.1 0.14287666 6.07950232 + layer.19.0 0.11209038 26.08187331 + layer.19.1 0.11164490 25.72126214 + layer.29.0 0.12578187 303.10836517 + layer.29.1 0.11401374 303.02093282 + layer.39.0 22.42121339 18533.15377268 + layer.39.1 25.87191330 20071.27921044 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 4909.31552537 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 661648 +BPFP 0.0514 bits/point +EBPFP 0.0514 equivalent bits/point +MSE 4909.315525 +---------------------- --------------------------------------------------------- +Time: 21.865s Load: 1.286s, Pack+Encode: 7.918s, Decode+Unpack: 12.661s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4909.3155 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001342-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,300B, BPFP=0.0381 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,988B, BPFP=0.0379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,316B, BPFP=0.0499 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,512B, BPFP=0.0501 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,376B, BPFP=0.0630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,100B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,688B, BPFP=0.0583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,276B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 7.774s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.907s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 6.02354570 + layer.9.1 0.00120738 6.07110967 + layer.19.0 0.01953576 27.01372473 + layer.19.1 0.08568942 27.28948285 + layer.29.0 0.14491542 318.97258039 + layer.29.1 0.15694472 327.59332219 + layer.39.0 8.88920166 18549.94205667 + layer.39.1 9.38273353 19337.09137217 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 4824.99964930 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 675556 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 4824.999649 +---------------------- --------------------------------------------------------- +Time: 21.973s Load: 1.291s, Pack+Encode: 7.774s, Decode+Unpack: 12.907s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4824.9996 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001421-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,044B, BPFP=0.0398 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,644B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,496B, BPFP=0.0507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,692B, BPFP=0.0508 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,972B, BPFP=0.0622 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,604B, BPFP=0.0619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 90,880B, BPFP=0.0565 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 90,664B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 7.851s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.878s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 6.48100662 + layer.9.1 0.14739036 6.47451672 + layer.19.0 0.16044666 28.94825991 + layer.19.1 0.14398357 29.28792333 + layer.29.0 0.50679369 341.88303884 + layer.29.1 0.43405572 341.44348933 + layer.39.0 123.83094556 18572.57306590 + layer.39.1 72.08861628 18416.41515441 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 4717.93830688 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 671996 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 4717.938307 +---------------------- --------------------------------------------------------- +Time: 22.045s Load: 1.316s, Pack+Encode: 7.851s, Decode+Unpack: 12.878s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4717.9383 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001428-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,992B, BPFP=0.0385 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,196B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 85,320B, BPFP=0.0531 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 85,332B, BPFP=0.0531 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,352B, BPFP=0.0649 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,796B, BPFP=0.0645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,120B, BPFP=0.0610 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,512B, BPFP=0.0613 +⌛️ [2/4] FRONTEND: Frontend time: 7.648s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.778s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 6.03569910 + layer.9.1 0.14229169 6.06694972 + layer.19.0 0.04567823 27.68187381 + layer.19.1 0.04432558 27.76172497 + layer.29.0 0.11507784 323.82402101 + layer.29.1 0.11363094 326.00163165 + layer.39.0 38.15331751 20327.74785100 + layer.39.1 50.78157832 20739.38745622 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 5223.06340093 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 699620 +BPFP 0.0544 bits/point +EBPFP 0.0544 equivalent bits/point +MSE 5223.063401 +---------------------- --------------------------------------------------------- +Time: 21.732s Load: 1.306s, Pack+Encode: 7.648s, Decode+Unpack: 12.778s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5223.0634 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001439-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.157s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,672B, BPFP=0.0377 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,580B, BPFP=0.0377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,508B, BPFP=0.0482 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 77,224B, BPFP=0.0480 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 94,536B, BPFP=0.0588 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 94,412B, BPFP=0.0587 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,632B, BPFP=0.0582 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,436B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 7.838s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.722s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 6.35987867 + layer.9.1 0.14417255 6.37102348 + layer.19.0 0.04986641 27.78667970 + layer.19.1 0.03935205 28.04648699 + layer.29.0 4.19438972 281.99196116 + layer.29.1 0.10069272 280.58317415 + layer.39.0 8.54645341 19026.40942375 + layer.39.1 8.58293537 19110.20439351 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 4845.96912768 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 653000 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 4845.969128 +---------------------- --------------------------------------------------------- +Time: 21.716s Load: 1.157s, Pack+Encode: 7.838s, Decode+Unpack: 12.722s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4845.9691 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001452-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,288B, BPFP=0.0369 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,852B, BPFP=0.0366 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,248B, BPFP=0.0493 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,448B, BPFP=0.0494 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,508B, BPFP=0.0644 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,188B, BPFP=0.0642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 102,368B, BPFP=0.0637 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 101,184B, BPFP=0.0629 +⌛️ [2/4] FRONTEND: Frontend time: 8.029s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 5.93355458 + layer.9.1 0.14191958 5.93911983 + layer.19.0 0.11064845 26.34105380 + layer.19.1 0.11258393 26.77731017 + layer.29.0 0.14067722 301.60510188 + layer.29.1 0.15898021 311.90086756 + layer.39.0 18.90648132 19847.70582617 + layer.39.1 12.01175482 20876.94619548 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 5175.39362868 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 687084 +BPFP 0.0534 bits/point +EBPFP 0.0534 equivalent bits/point +MSE 5175.393629 +---------------------- --------------------------------------------------------- +Time: 21.695s Load: 1.130s, Pack+Encode: 8.029s, Decode+Unpack: 12.536s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5175.3936 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001464-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.126s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,348B, BPFP=0.0363 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,604B, BPFP=0.0364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,996B, BPFP=0.0497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,884B, BPFP=0.0497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 103,688B, BPFP=0.0645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,112B, BPFP=0.0641 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 100,524B, BPFP=0.0625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,432B, BPFP=0.0612 +⌛️ [2/4] FRONTEND: Frontend time: 7.813s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 5.91296562 + layer.9.1 0.03265336 5.92111576 + layer.19.0 0.11338584 26.98955349 + layer.19.1 0.11737041 27.11044452 + layer.29.0 0.14518043 316.43407752 + layer.29.1 0.15176190 317.32272763 + layer.39.0 10.84722720 19493.73957338 + layer.39.1 10.76635501 19086.84368036 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 4910.03426729 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 682588 +BPFP 0.0531 bits/point +EBPFP 0.0531 equivalent bits/point +MSE 4910.034267 +---------------------- --------------------------------------------------------- +Time: 21.915s Load: 1.126s, Pack+Encode: 7.813s, Decode+Unpack: 12.976s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4910.0343 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001478-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,880B, BPFP=0.0366 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,636B, BPFP=0.0371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,508B, BPFP=0.0501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,224B, BPFP=0.0499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,284B, BPFP=0.0624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,460B, BPFP=0.0612 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 102,384B, BPFP=0.0637 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,764B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 7.846s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.833s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 6.02293134 + layer.9.1 0.14310633 6.07914105 + layer.19.0 0.11868409 27.73198971 + layer.19.1 0.12162521 27.49229445 + layer.29.0 0.16395149 305.44287249 + layer.29.1 0.12259847 293.24403056 + layer.39.0 330.19024594 22507.54536772 + layer.39.1 213.90321554 21690.49347342 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 5608.00651259 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 680140 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 5608.006513 +---------------------- --------------------------------------------------------- +Time: 21.988s Load: 1.309s, Pack+Encode: 7.846s, Decode+Unpack: 12.833s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5608.0065 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001495-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,712B, BPFP=0.0390 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,132B, BPFP=0.0393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,916B, BPFP=0.0509 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,760B, BPFP=0.0508 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,532B, BPFP=0.0631 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,192B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 91,228B, BPFP=0.0567 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 91,792B, BPFP=0.0571 +⌛️ [2/4] FRONTEND: Frontend time: 7.688s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 5.95753430 + layer.9.1 0.14187113 6.01205391 + layer.19.0 0.03719415 26.17477764 + layer.19.1 0.03715970 26.59553088 + layer.29.0 0.14992467 314.96645177 + layer.29.1 0.21581549 324.41945240 + layer.39.0 54.12547258 17512.19993633 + layer.39.1 37.28096148 18406.97357529 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 4577.91241406 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 676264 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4577.912414 +---------------------- --------------------------------------------------------- +Time: 21.970s Load: 1.316s, Pack+Encode: 7.688s, Decode+Unpack: 12.965s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4577.9124 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,176B, BPFP=0.0374 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,720B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,632B, BPFP=0.0501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,984B, BPFP=0.0504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,212B, BPFP=0.0629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,520B, BPFP=0.0631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 99,344B, BPFP=0.0618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,788B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 8.012s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.113s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 6.21837504 + layer.9.1 0.14222666 6.24466730 + layer.19.0 0.12883153 28.52727784 + layer.19.1 0.12450899 28.41795010 + layer.29.0 0.12456659 314.52365887 + layer.29.1 0.12180437 314.74681630 + layer.39.0 16.93397679 20143.61031519 + layer.39.1 11.63264585 20112.68003820 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 5119.37113735 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 684376 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 5119.371137 +---------------------- --------------------------------------------------------- +Time: 22.441s Load: 1.316s, Pack+Encode: 8.012s, Decode+Unpack: 13.113s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5119.3711 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001520-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,820B, BPFP=0.0391 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,940B, BPFP=0.0391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,880B, BPFP=0.0515 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,780B, BPFP=0.0515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,056B, BPFP=0.0610 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,668B, BPFP=0.0614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 91,412B, BPFP=0.0568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 91,676B, BPFP=0.0570 +⌛️ [2/4] FRONTEND: Frontend time: 8.043s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.153s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 6.05390712 + layer.9.1 0.14320703 6.04029867 + layer.19.0 0.18609190 28.40219327 + layer.19.1 0.20413370 28.31924298 + layer.29.0 0.16595908 314.21937679 + layer.29.1 0.17797341 316.24653773 + layer.39.0 9.44991518 16099.95542821 + layer.39.1 9.33992148 16184.68513212 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 4122.99026461 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 671232 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 4122.990265 +---------------------- --------------------------------------------------------- +Time: 22.499s Load: 1.304s, Pack+Encode: 8.043s, Decode+Unpack: 13.153s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4122.9903 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001571-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,652B, BPFP=0.0390 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,272B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,864B, BPFP=0.0509 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,844B, BPFP=0.0509 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,012B, BPFP=0.0616 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,016B, BPFP=0.0616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,688B, BPFP=0.0583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,616B, BPFP=0.0576 +⌛️ [2/4] FRONTEND: Frontend time: 7.961s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.112s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 6.10154758 + layer.9.1 0.14264699 6.06200441 + layer.19.0 0.04840791 27.81537030 + layer.19.1 0.04358378 28.01386153 + layer.29.0 4.25626169 302.77763849 + layer.29.1 4.25716892 307.26325215 + layer.39.0 36.32893585 18407.35052531 + layer.39.1 22.75239275 18065.60076409 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 4643.87312048 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 672964 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 4643.873120 +---------------------- --------------------------------------------------------- +Time: 22.382s Load: 1.309s, Pack+Encode: 7.961s, Decode+Unpack: 13.112s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4643.8731 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001605-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,496B, BPFP=0.0364 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,380B, BPFP=0.0369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,836B, BPFP=0.0490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,916B, BPFP=0.0497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,132B, BPFP=0.0598 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 97,960B, BPFP=0.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 90,980B, BPFP=0.0566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,720B, BPFP=0.0577 +⌛️ [2/4] FRONTEND: Frontend time: 7.970s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.110s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 6.17921865 + layer.9.1 0.14259219 6.18948173 + layer.19.0 0.15398767 29.25316629 + layer.19.1 0.14449470 29.47723157 + layer.29.0 0.17467273 328.15502627 + layer.29.1 0.17545724 331.56251990 + layer.39.0 16.22751761 17747.92741165 + layer.39.1 26.19674268 18046.14836039 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 4565.61155206 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 654420 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 4565.611552 +---------------------- --------------------------------------------------------- +Time: 22.392s Load: 1.312s, Pack+Encode: 7.970s, Decode+Unpack: 13.110s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4565.6116 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001617-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,344B, BPFP=0.0363 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 57,784B, BPFP=0.0359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,196B, BPFP=0.0480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 76,124B, BPFP=0.0473 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 94,128B, BPFP=0.0585 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 93,496B, BPFP=0.0581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,472B, BPFP=0.0581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 91,632B, BPFP=0.0570 +⌛️ [2/4] FRONTEND: Frontend time: 8.019s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.090s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 6.26066912 + layer.9.1 0.14283950 6.23085117 + layer.19.0 0.09585176 27.02866573 + layer.19.1 0.13229247 27.62972580 + layer.29.0 0.10926771 279.24970153 + layer.29.1 0.10983113 287.45463228 + layer.39.0 13.84559555 19597.96497931 + layer.39.1 12.75833856 20064.40496657 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 5037.02802394 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 642176 +BPFP 0.0499 bits/point +EBPFP 0.0499 equivalent bits/point +MSE 5037.028024 +---------------------- --------------------------------------------------------- +Time: 22.421s Load: 1.312s, Pack+Encode: 8.019s, Decode+Unpack: 13.090s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5037.0280 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001630-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,548B, BPFP=0.0376 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 58,628B, BPFP=0.0365 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,848B, BPFP=0.0484 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 77,544B, BPFP=0.0482 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 93,988B, BPFP=0.0584 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 95,220B, BPFP=0.0592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,280B, BPFP=0.0580 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,620B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 8.025s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 6.44209398 + layer.9.1 0.14345678 6.27074379 + layer.19.0 0.16166856 29.53881129 + layer.19.1 0.14880180 29.04335801 + layer.29.0 0.17070711 309.84188953 + layer.29.1 0.15868870 311.98909583 + layer.39.0 31.98565594 20068.15536453 + layer.39.1 38.57007372 19572.36930914 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 5041.70633326 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 650676 +BPFP 0.0506 bits/point +EBPFP 0.0506 equivalent bits/point +MSE 5041.706333 +---------------------- --------------------------------------------------------- +Time: 22.434s Load: 1.312s, Pack+Encode: 8.025s, Decode+Unpack: 13.097s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5041.7063 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001636-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,816B, BPFP=0.0378 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,880B, BPFP=0.0379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,760B, BPFP=0.0502 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,352B, BPFP=0.0506 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,236B, BPFP=0.0636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,252B, BPFP=0.0636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 98,676B, BPFP=0.0614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 97,704B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 8.048s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.948s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 5.91202730 + layer.9.1 0.03218400 5.90311605 + layer.19.0 0.03742503 25.92958303 + layer.19.1 0.04139693 26.97748528 + layer.29.0 0.11425402 303.52150987 + layer.29.1 0.11776626 313.89947469 + layer.39.0 23.31748448 19698.19929959 + layer.39.1 15.89369429 19194.65393187 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 4946.87455346 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 684676 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 4946.874553 +---------------------- --------------------------------------------------------- +Time: 22.308s Load: 1.311s, Pack+Encode: 8.048s, Decode+Unpack: 12.948s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4946.8746 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001639-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.267s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,676B, BPFP=0.0396 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,652B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,944B, BPFP=0.0503 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,748B, BPFP=0.0502 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,732B, BPFP=0.0633 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,440B, BPFP=0.0631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,464B, BPFP=0.0575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,764B, BPFP=0.0577 +⌛️ [2/4] FRONTEND: Frontend time: 7.959s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.898s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 6.36834283 + layer.9.1 0.14315520 6.38550372 + layer.19.0 0.04114968 27.53319255 + layer.19.1 0.04120060 27.52359917 + layer.29.0 0.18627036 330.91055794 + layer.29.1 0.17990809 329.35374881 + layer.39.0 46.02158449 17150.83349252 + layer.39.1 44.38447151 17203.05380452 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 4385.24528026 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677420 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 4385.245280 +---------------------- --------------------------------------------------------- +Time: 22.124s Load: 1.267s, Pack+Encode: 7.959s, Decode+Unpack: 12.898s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4385.2453 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001653-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.281s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,616B, BPFP=0.0383 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,128B, BPFP=0.0386 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,532B, BPFP=0.0507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,780B, BPFP=0.0509 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,992B, BPFP=0.0622 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,276B, BPFP=0.0617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,828B, BPFP=0.0583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,260B, BPFP=0.0586 +⌛️ [2/4] FRONTEND: Frontend time: 7.930s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64482133 5.90757012 + layer.9.1 0.03141260 5.92642483 + layer.19.0 3.18767318 26.10365678 + layer.19.1 3.18914595 25.69752368 + layer.29.0 4.14946039 280.84107370 + layer.29.1 4.13952905 274.75525310 + layer.39.0 7.50609877 17421.67717287 + layer.39.1 7.79272438 18580.76026743 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 4577.70861781 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 674412 +BPFP 0.0524 bits/point +EBPFP 0.0524 equivalent bits/point +MSE 4577.708618 +---------------------- --------------------------------------------------------- +Time: 21.611s Load: 1.281s, Pack+Encode: 7.930s, Decode+Unpack: 12.400s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4577.7086 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.237s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,396B, BPFP=0.0394 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,084B, BPFP=0.0392 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,228B, BPFP=0.0499 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,976B, BPFP=0.0504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,276B, BPFP=0.0636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 103,256B, BPFP=0.0642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 91,996B, BPFP=0.0572 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,008B, BPFP=0.0572 +⌛️ [2/4] FRONTEND: Frontend time: 7.944s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.084s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 6.13878194 + layer.9.1 0.14140505 6.11317430 + layer.19.0 0.11753838 26.45899495 + layer.19.1 0.11213660 26.14108017 + layer.29.0 0.21817993 323.31799188 + layer.29.1 4.26279853 313.33906399 + layer.39.0 8.71778059 17508.67112385 + layer.39.1 8.43609532 17683.67271570 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 4486.73161585 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677220 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 4486.731616 +---------------------- --------------------------------------------------------- +Time: 22.265s Load: 1.237s, Pack+Encode: 7.944s, Decode+Unpack: 13.084s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4486.7316 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001659-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.324s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,988B, BPFP=0.0379 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,364B, BPFP=0.0375 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 79,820B, BPFP=0.0496 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,884B, BPFP=0.0491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 95,752B, BPFP=0.0595 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 96,464B, BPFP=0.0600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 87,808B, BPFP=0.0546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 88,044B, BPFP=0.0547 +⌛️ [2/4] FRONTEND: Frontend time: 7.825s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.713s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 6.22358027 + layer.9.1 0.11967093 6.30358813 + layer.19.0 0.14332279 27.92251920 + layer.19.1 0.14205440 28.30044671 + layer.29.0 0.15356100 319.42269580 + layer.29.1 0.14462723 322.65102674 + layer.39.0 8.04224558 17284.08150271 + layer.39.1 10.17930073 17929.06462910 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 4490.49624858 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 648124 +BPFP 0.0504 bits/point +EBPFP 0.0504 equivalent bits/point +MSE 4490.496249 +---------------------- --------------------------------------------------------- +Time: 21.861s Load: 1.324s, Pack+Encode: 7.825s, Decode+Unpack: 12.713s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4490.4962 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001671-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.315s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,604B, BPFP=0.0396 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,732B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 84,212B, BPFP=0.0524 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 84,244B, BPFP=0.0524 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,320B, BPFP=0.0636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,328B, BPFP=0.0636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 100,120B, BPFP=0.0623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,008B, BPFP=0.0616 +⌛️ [2/4] FRONTEND: Frontend time: 8.028s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.620s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 5.99531648 + layer.9.1 0.00091860 6.02375214 + layer.19.0 3.15620088 25.49747045 + layer.19.1 3.15238324 25.76277708 + layer.29.0 4.13387767 268.86570758 + layer.29.1 4.13737010 275.23042025 + layer.39.0 41.03603550 21245.11047437 + layer.39.1 41.15380502 21365.52944922 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 5402.25192095 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 699568 +BPFP 0.0544 bits/point +EBPFP 0.0544 equivalent bits/point +MSE 5402.251921 +---------------------- --------------------------------------------------------- +Time: 21.963s Load: 1.315s, Pack+Encode: 8.028s, Decode+Unpack: 12.620s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5402.2519 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001694-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,504B, BPFP=0.0401 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 64,764B, BPFP=0.0403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 83,992B, BPFP=0.0522 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,908B, BPFP=0.0516 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 104,996B, BPFP=0.0653 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,880B, BPFP=0.0658 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 101,156B, BPFP=0.0629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 104,460B, BPFP=0.0650 +⌛️ [2/4] FRONTEND: Frontend time: 7.998s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.723s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 6.15831816 + layer.9.1 0.14279730 6.19830903 + layer.19.0 0.12708100 27.57833144 + layer.19.1 0.11978473 27.51501313 + layer.29.0 0.14591184 318.47063037 + layer.29.1 0.16402206 320.19886183 + layer.39.0 105.60261461 19829.82489653 + layer.39.1 191.64541547 21469.84527221 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 5250.72370409 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 712660 +BPFP 0.0554 bits/point +EBPFP 0.0554 equivalent bits/point +MSE 5250.723704 +---------------------- --------------------------------------------------------- +Time: 21.967s Load: 1.245s, Pack+Encode: 7.998s, Decode+Unpack: 12.723s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5250.7237 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001712-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.282s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,928B, BPFP=0.0354 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 56,944B, BPFP=0.0354 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,100B, BPFP=0.0486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,196B, BPFP=0.0486 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,928B, BPFP=0.0615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 99,232B, BPFP=0.0617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 100,880B, BPFP=0.0627 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 100,324B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 7.994s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.800s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 5.97775266 + layer.9.1 0.14187527 5.95576648 + layer.19.0 0.05966252 26.36689898 + layer.19.1 0.05602499 26.33185092 + layer.29.0 0.10851584 296.78175740 + layer.29.1 0.10663395 297.21028335 + layer.39.0 36.66006795 23109.18560968 + layer.39.1 37.39855191 22412.94874244 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 5772.59483274 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 669532 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 5772.594833 +---------------------- --------------------------------------------------------- +Time: 22.075s Load: 1.282s, Pack+Encode: 7.994s, Decode+Unpack: 12.800s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5772.5948 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001750-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,236B, BPFP=0.0375 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 60,144B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 85,176B, BPFP=0.0530 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 84,356B, BPFP=0.0525 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 101,204B, BPFP=0.0629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,252B, BPFP=0.0623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 91,004B, BPFP=0.0566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 89,984B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 7.774s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.954s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 6.06743225 + layer.9.1 0.11247108 6.01866568 + layer.19.0 0.01001183 28.73610862 + layer.19.1 3.17262087 28.53192156 + layer.29.0 0.16690336 324.66409981 + layer.29.1 0.17317613 326.55109838 + layer.39.0 33.55914965 17516.48137536 + layer.39.1 10.63762287 17600.08150271 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 4479.64152555 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 672356 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 4479.641526 +---------------------- --------------------------------------------------------- +Time: 22.045s Load: 1.317s, Pack+Encode: 7.774s, Decode+Unpack: 12.954s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4479.6415 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,140B, BPFP=0.0386 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,808B, BPFP=0.0372 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 83,108B, BPFP=0.0517 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 82,268B, BPFP=0.0512 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,944B, BPFP=0.0628 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,524B, BPFP=0.0625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,280B, BPFP=0.0580 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,660B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 7.821s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.826s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 6.03504992 + layer.9.1 0.03247940 6.08618001 + layer.19.0 0.20408508 28.36801825 + layer.19.1 0.20919449 28.42321812 + layer.29.0 0.13400092 310.23857848 + layer.29.1 0.12260655 312.30284941 + layer.39.0 13.98719058 17173.31423114 + layer.39.1 8.64389327 17756.49793060 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 4452.65825699 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 675732 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 4452.658257 +---------------------- --------------------------------------------------------- +Time: 21.894s Load: 1.247s, Pack+Encode: 7.821s, Decode+Unpack: 12.826s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4452.6583 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001838-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.255s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,836B, BPFP=0.0372 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,940B, BPFP=0.0373 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,100B, BPFP=0.0498 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,016B, BPFP=0.0498 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 98,504B, BPFP=0.0613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 98,076B, BPFP=0.0610 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,108B, BPFP=0.0585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,584B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 8.056s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.897s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 6.13090974 + layer.9.1 0.14463072 6.14969295 + layer.19.0 0.16931463 28.53403574 + layer.19.1 0.17979540 28.43748508 + layer.29.0 0.11737749 306.84374005 + layer.29.1 0.10948915 308.42713308 + layer.39.0 8.46774266 17324.08404967 + layer.39.1 8.48397517 17383.04871060 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 4423.95696961 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 664164 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 4423.956970 +---------------------- --------------------------------------------------------- +Time: 22.207s Load: 1.255s, Pack+Encode: 8.056s, Decode+Unpack: 12.897s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4423.9570 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001840-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.294s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,264B, BPFP=0.0381 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 62,552B, BPFP=0.0389 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,508B, BPFP=0.0501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,440B, BPFP=0.0506 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,228B, BPFP=0.0623 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,652B, BPFP=0.0626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,840B, BPFP=0.0584 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,376B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 8.086s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.656s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14223057 6.13134626 + layer.9.1 0.14268742 6.15953816 + layer.19.0 0.21739516 28.85590029 + layer.19.1 0.24972380 29.20826866 + layer.29.0 0.18828982 317.03577682 + layer.29.1 0.18108670 317.40317574 + layer.39.0 11.67542184 17462.79274117 + layer.39.1 15.11985385 17979.18115250 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 4518.34598745 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 674860 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 4518.345987 +---------------------- --------------------------------------------------------- +Time: 22.036s Load: 1.294s, Pack+Encode: 8.086s, Decode+Unpack: 12.656s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4518.3460 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001854-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,776B, BPFP=0.0378 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 59,924B, BPFP=0.0373 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,932B, BPFP=0.0503 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 80,268B, BPFP=0.0499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,524B, BPFP=0.0638 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,188B, BPFP=0.0629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,484B, BPFP=0.0594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,488B, BPFP=0.0588 +⌛️ [2/4] FRONTEND: Frontend time: 7.794s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.721s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 6.07030815 + layer.9.1 0.14270393 6.07543129 + layer.19.0 0.11367196 27.84509810 + layer.19.1 0.12267420 27.71680745 + layer.29.0 0.13560262 319.72096864 + layer.29.1 0.14809222 323.45803486 + layer.39.0 10.32325245 18162.87679083 + layer.39.1 8.35688960 17912.50175103 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 4598.28314879 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 675584 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 4598.283149 +---------------------- --------------------------------------------------------- +Time: 21.820s Load: 1.305s, Pack+Encode: 7.794s, Decode+Unpack: 12.721s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4598.2831 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001855-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,696B, BPFP=0.0384 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 61,168B, BPFP=0.0380 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,760B, BPFP=0.0502 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,864B, BPFP=0.0497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 96,888B, BPFP=0.0602 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 97,872B, BPFP=0.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 87,936B, BPFP=0.0547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 91,072B, BPFP=0.0566 +⌛️ [2/4] FRONTEND: Frontend time: 7.956s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61171023 6.01987014 + layer.9.1 2.72679972 5.97652706 + layer.19.0 0.11263356 27.28182705 + layer.19.1 0.10212393 27.04651435 + layer.29.0 4.19513435 298.35820599 + layer.29.1 4.21594343 305.10408707 + layer.39.0 8.80532175 15262.53677173 + layer.39.1 9.27097449 16332.43680357 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 4033.09507587 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 657256 +BPFP 0.0511 bits/point +EBPFP 0.0511 equivalent bits/point +MSE 4033.095076 +---------------------- --------------------------------------------------------- +Time: 22.155s Load: 1.162s, Pack+Encode: 7.956s, Decode+Unpack: 13.038s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4033.0951 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001857-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,036B, BPFP=0.0398 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 64,448B, BPFP=0.0401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 83,864B, BPFP=0.0521 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 84,452B, BPFP=0.0525 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,904B, BPFP=0.0640 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 104,368B, BPFP=0.0649 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,112B, BPFP=0.0591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 96,252B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 7.881s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.808s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 6.36372212 + layer.9.1 0.14997165 6.36310092 + layer.19.0 0.15685862 27.79490509 + layer.19.1 0.13652294 27.93841780 + layer.29.0 0.22636045 314.36144938 + layer.29.1 0.21023706 320.51440624 + layer.39.0 31.35143565 17353.02260427 + layer.39.1 33.65704095 18243.26520216 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 4537.45297600 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 695436 +BPFP 0.0541 bits/point +EBPFP 0.0541 equivalent bits/point +MSE 4537.452976 +---------------------- --------------------------------------------------------- +Time: 21.877s Load: 1.187s, Pack+Encode: 7.881s, Decode+Unpack: 12.808s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4537.4530 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,348B, BPFP=0.0400 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,248B, BPFP=0.0393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 81,948B, BPFP=0.0510 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 81,936B, BPFP=0.0509 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,356B, BPFP=0.0624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 101,372B, BPFP=0.0630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,640B, BPFP=0.0607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 99,956B, BPFP=0.0622 +⌛️ [2/4] FRONTEND: Frontend time: 7.810s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.652s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 6.12302387 + layer.9.1 0.14194651 6.11038172 + layer.19.0 0.13165920 28.32011849 + layer.19.1 0.11547583 28.29587014 + layer.29.0 4.19202371 300.16330388 + layer.29.1 0.11136677 301.80402340 + layer.39.0 9.51575185 18752.26488379 + layer.39.1 9.66679849 19289.98280802 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 4839.13305167 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 690804 +BPFP 0.0537 bits/point +EBPFP 0.0537 equivalent bits/point +MSE 4839.133052 +---------------------- --------------------------------------------------------- +Time: 21.631s Load: 1.168s, Pack+Encode: 7.810s, Decode+Unpack: 12.652s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4839.1331 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,632B, BPFP=0.0396 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 63,708B, BPFP=0.0396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 84,704B, BPFP=0.0527 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 84,960B, BPFP=0.0528 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 102,052B, BPFP=0.0635 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 102,128B, BPFP=0.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 93,000B, BPFP=0.0578 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 92,160B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 7.940s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.900s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.60361947 5.98565719 + layer.9.1 2.64162177 5.95707042 + layer.19.0 3.15421573 26.59459567 + layer.19.1 3.18597002 26.66314719 + layer.29.0 4.16148507 297.00789955 + layer.29.1 4.16879732 293.85870344 + layer.39.0 7.32495125 17032.96020376 + layer.39.1 7.16856507 16525.56638013 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 4276.82420717 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 686344 +BPFP 0.0533 bits/point +EBPFP 0.0533 equivalent bits/point +MSE 4276.824207 +---------------------- --------------------------------------------------------- +Time: 22.007s Load: 1.166s, Pack+Encode: 7.940s, Decode+Unpack: 12.900s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4276.8242 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0523 bits/point +Avg EBPFP 0.0523 equivalent bits/point +Avg MSE 4861.016716 +Avg Time 21.978s +------------------------ ---------------------------- diff --git a/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..6aaa37d273c9d8eb400fb27f0cfc51783b51f306 --- /dev/null +++ b/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_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/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 559 +Loaded hyperprior-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 hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.294s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 302,316B, BPFP=0.1880 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 311,060B, BPFP=0.1934 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 656,916B, BPFP=0.4085 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 676,184B, BPFP=0.4205 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 715,036B, BPFP=0.4446 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 767,392B, BPFP=0.4772 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 455,972B, BPFP=0.2835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 474,036B, BPFP=0.2948 +⌛️ [2/4] FRONTEND: Frontend time: 8.308s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.163s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11100285 4.61396400 + layer.9.1 0.11103876 4.59189077 + layer.19.0 0.02553116 33.24525181 + layer.19.1 0.10833414 27.33928038 + layer.29.0 0.30844607 273.60247533 + layer.29.1 0.33610574 240.20670169 + layer.39.0 10.03071710 2430.95733843 + layer.39.1 10.11984639 2651.48615091 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 708.25538166 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4358912 +BPFP 0.3388 bits/point +EBPFP 0.3388 equivalent bits/point +MSE 708.255382 +---------------------- --------------------------------------------------------- +Time: 22.766s Load: 1.294s, Pack+Encode: 8.308s, Decode+Unpack: 13.163s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 708.2554 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000001-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 290,688B, BPFP=0.1808 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 297,592B, BPFP=0.1850 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 611,700B, BPFP=0.3804 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 664,316B, BPFP=0.4131 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 713,788B, BPFP=0.4438 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 738,652B, BPFP=0.4593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 519,340B, BPFP=0.3229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 522,696B, BPFP=0.3250 +⌛️ [2/4] FRONTEND: Frontend time: 7.929s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.084s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61021196 4.66758136 + layer.9.1 2.61901253 4.63230417 + layer.19.0 3.15140481 8.02727162 + layer.19.1 3.16250889 12.12186107 + layer.29.0 4.15625404 57.20214502 + layer.29.1 4.15938147 75.07665254 + layer.39.0 10.95910936 2429.66125438 + layer.39.1 9.06533984 2679.48455906 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 658.85920365 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4358772 +BPFP 0.3388 bits/point +EBPFP 0.3388 equivalent bits/point +MSE 658.859204 +---------------------- --------------------------------------------------------- +Time: 22.325s Load: 1.312s, Pack+Encode: 7.929s, Decode+Unpack: 13.084s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 658.8592 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000045-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,644B, BPFP=0.2062 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 331,832B, BPFP=0.2063 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 817,904B, BPFP=0.5086 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 830,432B, BPFP=0.5164 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 923,912B, BPFP=0.5745 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 946,332B, BPFP=0.5884 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,008B, BPFP=0.3544 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 580,564B, BPFP=0.3610 +⌛️ [2/4] FRONTEND: Frontend time: 7.918s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 4.54338754 + layer.9.1 0.14253284 4.52459501 + layer.19.0 0.09744245 66.72034682 + layer.19.1 0.13747554 53.71576031 + layer.29.0 4.19766265 171.53738857 + layer.29.1 4.20130152 128.93512217 + layer.39.0 38.53896798 2854.79879019 + layer.39.1 35.26563495 2992.65616046 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 784.67894389 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5332628 +BPFP 0.4145 bits/point +EBPFP 0.4145 equivalent bits/point +MSE 784.678944 +---------------------- --------------------------------------------------------- +Time: 22.242s Load: 1.310s, Pack+Encode: 7.918s, Decode+Unpack: 13.014s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 784.6789 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000064-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,816B, BPFP=0.2020 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 320,724B, BPFP=0.1994 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 821,140B, BPFP=0.5106 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 767,164B, BPFP=0.4770 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 900,596B, BPFP=0.5600 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 881,844B, BPFP=0.5483 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 586,188B, BPFP=0.3645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 599,184B, BPFP=0.3726 +⌛️ [2/4] FRONTEND: Frontend time: 7.930s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.142s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 4.55000180 + layer.9.1 0.03225276 4.53727198 + layer.19.0 0.11899935 17.79505308 + layer.19.1 0.11456829 12.66759566 + layer.29.0 0.13249551 106.40098695 + layer.29.1 0.12471250 98.59558063 + layer.39.0 10.78219516 2835.99554282 + layer.39.1 9.99374328 2735.70741802 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 727.03118137 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5201656 +BPFP 0.4043 bits/point +EBPFP 0.4043 equivalent bits/point +MSE 727.031181 +---------------------- --------------------------------------------------------- +Time: 22.384s Load: 1.312s, Pack+Encode: 7.930s, Decode+Unpack: 13.142s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 727.0312 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000092-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.314s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 294,024B, BPFP=0.1828 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 291,460B, BPFP=0.1812 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 646,720B, BPFP=0.4021 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 619,708B, BPFP=0.3853 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 744,556B, BPFP=0.4630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 721,776B, BPFP=0.4488 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 509,920B, BPFP=0.3171 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 511,272B, BPFP=0.3179 +⌛️ [2/4] FRONTEND: Frontend time: 7.946s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 4.60634302 + layer.9.1 0.03227402 4.63823474 + layer.19.0 3.18865969 12.83250880 + layer.19.1 3.19251184 8.08166873 + layer.29.0 0.19572780 190.49245861 + layer.29.1 0.14992644 106.51329195 + layer.39.0 12.23891426 2484.26074499 + layer.39.1 9.64680585 2696.76090417 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 688.52326938 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4339436 +BPFP 0.3373 bits/point +EBPFP 0.3373 equivalent bits/point +MSE 688.523269 +---------------------- --------------------------------------------------------- +Time: 22.244s Load: 1.314s, Pack+Encode: 7.946s, Decode+Unpack: 12.983s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 688.5233 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000096-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.303s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 311,524B, BPFP=0.1937 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 316,012B, BPFP=0.1965 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 654,508B, BPFP=0.4070 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 691,464B, BPFP=0.4300 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 802,844B, BPFP=0.4992 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 815,968B, BPFP=0.5074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 540,676B, BPFP=0.3362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 550,452B, BPFP=0.3423 +⌛️ [2/4] FRONTEND: Frontend time: 7.875s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.092s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 4.62433124 + layer.9.1 0.14248663 4.58338028 + layer.19.0 0.04071400 8.09943278 + layer.19.1 0.03715074 12.44668796 + layer.29.0 4.22673132 120.75241762 + layer.29.1 4.22861263 129.20212512 + layer.39.0 10.70292353 2634.43075454 + layer.39.1 9.44238934 2705.60235594 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 702.46768568 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4683448 +BPFP 0.3640 bits/point +EBPFP 0.3640 equivalent bits/point +MSE 702.467686 +---------------------- --------------------------------------------------------- +Time: 22.270s Load: 1.303s, Pack+Encode: 7.875s, Decode+Unpack: 13.092s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 702.4677 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000133-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 333,044B, BPFP=0.2071 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 335,308B, BPFP=0.2085 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 836,820B, BPFP=0.5203 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 801,800B, BPFP=0.4986 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 865,412B, BPFP=0.5381 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 845,512B, BPFP=0.5258 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 549,296B, BPFP=0.3416 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 523,016B, BPFP=0.3252 +⌛️ [2/4] FRONTEND: Frontend time: 7.955s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 4.43476152 + layer.9.1 0.14203072 4.46267268 + layer.19.0 0.04969746 39.42668786 + layer.19.1 0.04852902 34.50767819 + layer.29.0 0.13952979 139.38614295 + layer.29.1 0.11857529 96.73795169 + layer.39.0 52.16041866 2355.83365170 + layer.39.1 64.85207736 2244.80149634 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 614.94888037 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5090208 +BPFP 0.3956 bits/point +EBPFP 0.3956 equivalent bits/point +MSE 614.948880 +---------------------- --------------------------------------------------------- +Time: 22.341s Load: 1.306s, Pack+Encode: 7.955s, Decode+Unpack: 13.080s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 614.9489 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000196-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.318s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 322,372B, BPFP=0.2005 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 323,140B, BPFP=0.2009 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 705,880B, BPFP=0.4389 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 727,832B, BPFP=0.4526 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 853,312B, BPFP=0.5306 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 862,388B, BPFP=0.5362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 550,600B, BPFP=0.3424 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 549,512B, BPFP=0.3417 +⌛️ [2/4] FRONTEND: Frontend time: 7.906s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.167s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 4.54759724 + layer.9.1 0.14255715 4.56346444 + layer.19.0 0.12077588 34.99868175 + layer.19.1 0.12364273 29.74248348 + layer.29.0 4.20710867 72.46995384 + layer.29.1 4.21108798 91.85568887 + layer.39.0 8.84959445 2775.32760267 + layer.39.1 9.12830806 2848.20916905 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 732.71433017 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4895036 +BPFP 0.3805 bits/point +EBPFP 0.3805 equivalent bits/point +MSE 732.714330 +---------------------- --------------------------------------------------------- +Time: 22.391s Load: 1.318s, Pack+Encode: 7.906s, Decode+Unpack: 13.167s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 732.7143 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000268-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 348,144B, BPFP=0.2165 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 354,220B, BPFP=0.2203 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 805,928B, BPFP=0.5011 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 832,144B, BPFP=0.5174 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 917,568B, BPFP=0.5706 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 937,852B, BPFP=0.5832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 613,588B, BPFP=0.3815 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 634,500B, BPFP=0.3945 +⌛️ [2/4] FRONTEND: Frontend time: 8.004s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.216s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 4.45036445 + layer.9.1 0.14262173 4.48411042 + layer.19.0 0.13202983 26.29570101 + layer.19.1 0.12978742 35.02564122 + layer.29.0 0.12169007 222.26386899 + layer.29.1 0.13371499 203.02282315 + layer.39.0 71.22791309 3178.41515441 + layer.39.1 35.82807525 3372.50111429 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 880.80734724 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5443944 +BPFP 0.4231 bits/point +EBPFP 0.4231 equivalent bits/point +MSE 880.807347 +---------------------- --------------------------------------------------------- +Time: 22.525s Load: 1.306s, Pack+Encode: 8.004s, Decode+Unpack: 13.216s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 880.8073 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000315-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.264s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,388B, BPFP=0.1899 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 308,884B, BPFP=0.1921 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 759,988B, BPFP=0.4726 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 778,200B, BPFP=0.4839 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 904,640B, BPFP=0.5625 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 863,564B, BPFP=0.5370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 580,668B, BPFP=0.3611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 554,248B, BPFP=0.3446 +⌛️ [2/4] FRONTEND: Frontend time: 7.962s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.113s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 4.65647665 + layer.9.1 0.14121198 4.60575168 + layer.19.0 0.08207523 21.96625279 + layer.19.1 0.11558007 39.43259014 + layer.29.0 0.16338114 179.71197469 + layer.29.1 0.15213004 154.32385188 + layer.39.0 27.31461666 3106.18210761 + layer.39.1 28.69002706 3042.19165871 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 819.13383302 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5055580 +BPFP 0.3930 bits/point +EBPFP 0.3930 equivalent bits/point +MSE 819.133833 +---------------------- --------------------------------------------------------- +Time: 22.339s Load: 1.264s, Pack+Encode: 7.962s, Decode+Unpack: 13.113s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 819.1338 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000322-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 322,468B, BPFP=0.2005 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 329,216B, BPFP=0.2047 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 787,340B, BPFP=0.4896 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 816,180B, BPFP=0.5075 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 945,216B, BPFP=0.5878 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 933,564B, BPFP=0.5805 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 592,776B, BPFP=0.3686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 603,624B, BPFP=0.3753 +⌛️ [2/4] FRONTEND: Frontend time: 7.905s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 4.54711129 + layer.9.1 0.11112548 4.58661777 + layer.19.0 0.11343976 25.76856992 + layer.19.1 0.08227446 44.15346426 + layer.29.0 0.11178890 120.51349093 + layer.29.1 4.21559211 81.09610295 + layer.39.0 9.18455757 3023.75262655 + layer.39.1 8.88372284 3199.96943649 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 813.04842752 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5330384 +BPFP 0.4143 bits/point +EBPFP 0.4143 equivalent bits/point +MSE 813.048428 +---------------------- --------------------------------------------------------- +Time: 22.250s Load: 1.310s, Pack+Encode: 7.905s, Decode+Unpack: 13.035s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 813.0484 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000347-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 361,472B, BPFP=0.2248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 347,240B, BPFP=0.2159 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 706,092B, BPFP=0.4391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 734,728B, BPFP=0.4569 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 897,908B, BPFP=0.5583 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 888,004B, BPFP=0.5522 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,596B, BPFP=0.3119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 515,012B, BPFP=0.3202 +⌛️ [2/4] FRONTEND: Frontend time: 7.959s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 4.55857323 + layer.9.1 0.14561824 4.55292838 + layer.19.0 0.12576092 71.76519719 + layer.19.1 0.12606844 32.14696006 + layer.29.0 0.19770402 129.35279370 + layer.29.1 0.18863435 213.12659185 + layer.39.0 84.70259273 2669.01560013 + layer.39.1 43.66404011 2618.57004139 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 717.88608574 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4952052 +BPFP 0.3849 bits/point +EBPFP 0.3849 equivalent bits/point +MSE 717.886086 +---------------------- --------------------------------------------------------- +Time: 22.368s Load: 1.312s, Pack+Encode: 7.959s, Decode+Unpack: 13.097s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 717.8861 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000352-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 298,704B, BPFP=0.1857 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 299,280B, BPFP=0.1861 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 566,376B, BPFP=0.3522 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 537,696B, BPFP=0.3343 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 622,064B, BPFP=0.3868 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 607,484B, BPFP=0.3777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,436B, BPFP=0.2658 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 416,384B, BPFP=0.2589 +⌛️ [2/4] FRONTEND: Frontend time: 7.882s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.089s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 4.52224299 + layer.9.1 0.14295322 4.54987526 + layer.19.0 0.05949541 18.26146878 + layer.19.1 0.07012351 41.55054620 + layer.29.0 4.21949463 66.11197170 + layer.29.1 4.23773965 109.48755372 + layer.39.0 8.48589099 2577.23654887 + layer.39.1 10.46205428 2321.95383636 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 642.95925549 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3775424 +BPFP 0.2935 bits/point +EBPFP 0.2935 equivalent bits/point +MSE 642.959255 +---------------------- --------------------------------------------------------- +Time: 22.280s Load: 1.310s, Pack+Encode: 7.882s, Decode+Unpack: 13.089s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 642.9593 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000360-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 302,664B, BPFP=0.1882 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 299,568B, BPFP=0.1863 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 566,720B, BPFP=0.3524 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 538,136B, BPFP=0.3346 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 568,992B, BPFP=0.3538 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 559,172B, BPFP=0.3477 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,824B, BPFP=0.2586 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 407,772B, BPFP=0.2536 +⌛️ [2/4] FRONTEND: Frontend time: 7.889s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.094s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 4.56240082 + layer.9.1 0.00177230 4.53107465 + layer.19.0 0.01183476 18.36351381 + layer.19.1 0.01005667 9.56700382 + layer.29.0 4.18449569 60.52635506 + layer.29.1 4.18053255 53.05958493 + layer.39.0 7.97218927 2237.30706781 + layer.39.1 7.92115618 2444.26424706 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 604.02265599 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3658848 +BPFP 0.2844 bits/point +EBPFP 0.2844 equivalent bits/point +MSE 604.022656 +---------------------- --------------------------------------------------------- +Time: 22.292s Load: 1.309s, Pack+Encode: 7.889s, Decode+Unpack: 13.094s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 604.0227 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000389-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,588B, BPFP=0.2018 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 323,384B, BPFP=0.2011 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 685,852B, BPFP=0.4265 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 677,832B, BPFP=0.4215 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 816,720B, BPFP=0.5078 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 809,560B, BPFP=0.5034 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 543,348B, BPFP=0.3379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 532,552B, BPFP=0.3311 +⌛️ [2/4] FRONTEND: Frontend time: 7.914s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.242s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 4.61469029 + layer.9.1 0.03324844 4.56672493 + layer.19.0 0.13337831 48.40164856 + layer.19.1 0.12266011 17.59325006 + layer.29.0 4.22871927 148.29709885 + layer.29.1 4.21185188 136.83428844 + layer.39.0 10.68945623 2666.72572429 + layer.39.1 11.70080065 2454.19484241 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 685.15353348 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4713836 +BPFP 0.3664 bits/point +EBPFP 0.3664 equivalent bits/point +MSE 685.153533 +---------------------- --------------------------------------------------------- +Time: 22.463s Load: 1.306s, Pack+Encode: 7.914s, Decode+Unpack: 13.242s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 685.1535 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000429-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.268s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 334,792B, BPFP=0.2082 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 326,648B, BPFP=0.2031 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 703,440B, BPFP=0.4374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 691,696B, BPFP=0.4301 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 787,660B, BPFP=0.4898 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 786,868B, BPFP=0.4893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 502,736B, BPFP=0.3126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 497,188B, BPFP=0.3092 +⌛️ [2/4] FRONTEND: Frontend time: 7.832s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.187s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 4.51690345 + layer.9.1 0.14233285 4.53565743 + layer.19.0 0.14139387 57.77176855 + layer.19.1 0.13524239 39.52549944 + layer.29.0 0.16019033 90.96905842 + layer.29.1 0.14649145 142.06480818 + layer.39.0 12.41561455 2392.05093919 + layer.39.1 10.59172910 2408.77554919 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 642.52627298 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4631028 +BPFP 0.3600 bits/point +EBPFP 0.3600 equivalent bits/point +MSE 642.526273 +---------------------- --------------------------------------------------------- +Time: 22.287s Load: 1.268s, Pack+Encode: 7.832s, Decode+Unpack: 13.187s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 642.5263 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000436-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,304B, BPFP=0.1923 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 312,272B, BPFP=0.1942 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 756,660B, BPFP=0.4705 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 754,512B, BPFP=0.4692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 772,200B, BPFP=0.4802 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 766,272B, BPFP=0.4765 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 485,508B, BPFP=0.3019 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 484,684B, BPFP=0.3014 +⌛️ [2/4] FRONTEND: Frontend time: 7.865s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.105s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 4.53155127 + layer.9.1 0.03247534 4.51989812 + layer.19.0 0.03739121 11.98245110 + layer.19.1 0.03736199 7.56662078 + layer.29.0 4.17784350 73.61422019 + layer.29.1 4.17623735 49.39116225 + layer.39.0 10.57947434 2444.59296402 + layer.39.1 10.58388675 2509.76010825 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 638.24487200 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4641412 +BPFP 0.3608 bits/point +EBPFP 0.3608 equivalent bits/point +MSE 638.244872 +---------------------- --------------------------------------------------------- +Time: 22.281s Load: 1.311s, Pack+Encode: 7.865s, Decode+Unpack: 13.105s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 638.2449 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000442-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 311,304B, BPFP=0.1936 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 303,396B, BPFP=0.1887 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 632,528B, BPFP=0.3933 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 640,220B, BPFP=0.3981 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 726,180B, BPFP=0.4516 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 734,628B, BPFP=0.4568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,940B, BPFP=0.3096 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 491,432B, BPFP=0.3056 +⌛️ [2/4] FRONTEND: Frontend time: 7.899s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.148s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 4.59952481 + layer.9.1 0.03247583 4.58696940 + layer.19.0 0.05000294 7.71328050 + layer.19.1 0.04728991 12.10712651 + layer.29.0 4.17616118 54.69769281 + layer.29.1 4.18555745 93.02163921 + layer.39.0 14.92630606 2219.47150589 + layer.39.1 15.22664209 2235.12304998 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 578.91509864 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4337628 +BPFP 0.3372 bits/point +EBPFP 0.3372 equivalent bits/point +MSE 578.915099 +---------------------- --------------------------------------------------------- +Time: 22.350s Load: 1.304s, Pack+Encode: 7.899s, Decode+Unpack: 13.148s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 578.9151 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000447-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 311,548B, BPFP=0.1937 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 305,216B, BPFP=0.1898 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 653,288B, BPFP=0.4062 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 635,004B, BPFP=0.3949 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 643,908B, BPFP=0.4004 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 622,380B, BPFP=0.3870 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,756B, BPFP=0.2759 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 445,680B, BPFP=0.2771 +⌛️ [2/4] FRONTEND: Frontend time: 7.898s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.790s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 4.53833871 + layer.9.1 0.11516861 4.55889098 + layer.19.0 0.04822375 8.05471983 + layer.19.1 0.02465675 8.18042995 + layer.29.0 0.12445424 68.96326807 + layer.29.1 4.21809243 47.81857390 + layer.39.0 56.99443848 2463.43346068 + layer.39.1 29.63154648 2145.41738300 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 593.87063314 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4060780 +BPFP 0.3156 bits/point +EBPFP 0.3156 equivalent bits/point +MSE 593.870633 +---------------------- --------------------------------------------------------- +Time: 21.997s Load: 1.309s, Pack+Encode: 7.898s, Decode+Unpack: 12.790s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 593.8706 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000455-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 316,536B, BPFP=0.1968 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 325,908B, BPFP=0.2027 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 731,664B, BPFP=0.4550 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 790,832B, BPFP=0.4918 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 853,788B, BPFP=0.5309 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 893,808B, BPFP=0.5558 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 572,580B, BPFP=0.3560 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 582,036B, BPFP=0.3619 +⌛️ [2/4] FRONTEND: Frontend time: 7.863s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 4.56801582 + layer.9.1 0.14323425 4.59891045 + layer.19.0 0.12097352 7.46418711 + layer.19.1 0.11863553 21.33302243 + layer.29.0 0.18810310 136.23555396 + layer.29.1 0.22084548 301.71296960 + layer.39.0 11.17468934 2760.02833493 + layer.39.1 12.52284677 3087.66443808 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 790.45067905 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5067152 +BPFP 0.3939 bits/point +EBPFP 0.3939 equivalent bits/point +MSE 790.450679 +---------------------- --------------------------------------------------------- +Time: 22.218s Load: 1.308s, Pack+Encode: 7.863s, Decode+Unpack: 13.047s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 790.4507 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000474-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 320,988B, BPFP=0.1996 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 328,560B, BPFP=0.2043 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 742,984B, BPFP=0.4620 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 714,108B, BPFP=0.4440 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 860,456B, BPFP=0.5350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 866,592B, BPFP=0.5389 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,136B, BPFP=0.3527 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 582,960B, BPFP=0.3625 +⌛️ [2/4] FRONTEND: Frontend time: 7.853s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.162s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 4.56506841 + layer.9.1 0.14176414 4.54975743 + layer.19.0 0.11837582 20.85854923 + layer.19.1 0.11399856 7.23493837 + layer.29.0 0.14311602 243.73499682 + layer.29.1 0.14520382 218.63741643 + layer.39.0 14.59939236 2918.99299586 + layer.39.1 17.09091825 2901.87201528 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 790.05571723 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4983784 +BPFP 0.3874 bits/point +EBPFP 0.3874 equivalent bits/point +MSE 790.055717 +---------------------- --------------------------------------------------------- +Time: 22.327s Load: 1.312s, Pack+Encode: 7.853s, Decode+Unpack: 13.162s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 790.0557 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000476-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 296,516B, BPFP=0.1844 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 297,576B, BPFP=0.1850 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 651,380B, BPFP=0.4050 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 645,612B, BPFP=0.4015 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 766,592B, BPFP=0.4767 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 784,568B, BPFP=0.4879 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 546,044B, BPFP=0.3395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 564,088B, BPFP=0.3508 +⌛️ [2/4] FRONTEND: Frontend time: 7.940s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 4.61249092 + layer.9.1 0.14209374 4.58500011 + layer.19.0 0.05177973 13.00922900 + layer.19.1 0.05586525 26.17447917 + layer.29.0 0.12731753 137.45169731 + layer.29.1 0.12791453 91.18046601 + layer.39.0 10.91882437 2609.56813117 + layer.39.1 9.86751520 2674.46418338 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 695.13070963 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4552376 +BPFP 0.3538 bits/point +EBPFP 0.3538 equivalent bits/point +MSE 695.130710 +---------------------- --------------------------------------------------------- +Time: 22.317s Load: 1.310s, Pack+Encode: 7.940s, Decode+Unpack: 13.067s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 695.1307 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000479-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 322,676B, BPFP=0.2006 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 326,800B, BPFP=0.2032 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 633,140B, BPFP=0.3937 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 680,076B, BPFP=0.4229 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 821,096B, BPFP=0.5106 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 868,320B, BPFP=0.5399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 536,984B, BPFP=0.3339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 551,924B, BPFP=0.3432 +⌛️ [2/4] FRONTEND: Frontend time: 7.538s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.682s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 4.52699056 + layer.9.1 0.03257298 4.51770995 + layer.19.0 0.03929411 30.02680277 + layer.19.1 0.03736255 7.54152116 + layer.29.0 4.19976128 54.92253661 + layer.29.1 4.19887364 74.17521291 + layer.39.0 17.81771704 2494.27363897 + layer.39.1 13.24929237 2583.81311684 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 656.72469122 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4741016 +BPFP 0.3685 bits/point +EBPFP 0.3685 equivalent bits/point +MSE 656.724691 +---------------------- --------------------------------------------------------- +Time: 21.538s Load: 1.317s, Pack+Encode: 7.538s, Decode+Unpack: 12.682s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 656.7247 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000489-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,904B, BPFP=0.2045 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 331,552B, BPFP=0.2062 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 767,064B, BPFP=0.4770 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 784,700B, BPFP=0.4879 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 841,868B, BPFP=0.5235 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 849,464B, BPFP=0.5282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 539,604B, BPFP=0.3355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 516,940B, BPFP=0.3214 +⌛️ [2/4] FRONTEND: Frontend time: 7.816s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.884s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 4.53859365 + layer.9.1 0.14206870 4.48101875 + layer.19.0 0.11541664 17.02446723 + layer.19.1 0.11639375 25.73721297 + layer.29.0 4.18928181 62.00402937 + layer.29.1 4.20210771 87.30081980 + layer.39.0 272.14109758 3058.49347342 + layer.39.1 217.56435053 3147.45622413 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 800.87947991 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4960096 +BPFP 0.3855 bits/point +EBPFP 0.3855 equivalent bits/point +MSE 800.879480 +---------------------- --------------------------------------------------------- +Time: 21.989s Load: 1.289s, Pack+Encode: 7.816s, Decode+Unpack: 12.884s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 800.8795 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 338,988B, BPFP=0.2108 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 337,304B, BPFP=0.2097 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 761,784B, BPFP=0.4737 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 817,600B, BPFP=0.5084 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 936,744B, BPFP=0.5825 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 965,460B, BPFP=0.6003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,580B, BPFP=0.3405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 572,760B, BPFP=0.3562 +⌛️ [2/4] FRONTEND: Frontend time: 7.978s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.851s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 4.52906711 + layer.9.1 0.14265629 4.58087716 + layer.19.0 0.15235519 21.04184326 + layer.19.1 0.14002283 16.79018674 + layer.29.0 4.20702410 171.54636262 + layer.29.1 4.22502724 125.01137178 + layer.39.0 9.71896204 2719.01241643 + layer.39.1 14.02077861 2885.03788602 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 743.44375139 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5278220 +BPFP 0.4103 bits/point +EBPFP 0.4103 equivalent bits/point +MSE 743.443751 +---------------------- --------------------------------------------------------- +Time: 22.140s Load: 1.310s, Pack+Encode: 7.978s, Decode+Unpack: 12.851s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 743.4438 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000524-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.288s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 314,420B, BPFP=0.1955 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 310,540B, BPFP=0.1931 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 724,708B, BPFP=0.4506 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 660,672B, BPFP=0.4108 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 888,424B, BPFP=0.5524 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 818,256B, BPFP=0.5088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 617,792B, BPFP=0.3842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 602,828B, BPFP=0.3748 +⌛️ [2/4] FRONTEND: Frontend time: 7.990s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.098s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 4.55671369 + layer.9.1 0.14327397 4.59237641 + layer.19.0 0.03872790 7.68544738 + layer.19.1 0.03991431 7.78049574 + layer.29.0 0.11363128 116.01482410 + layer.29.1 0.09618797 54.49306053 + layer.39.0 113.00349212 3180.20916905 + layer.39.1 66.70960681 3163.89875836 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 817.40385566 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4937640 +BPFP 0.3838 bits/point +EBPFP 0.3838 equivalent bits/point +MSE 817.403856 +---------------------- --------------------------------------------------------- +Time: 22.376s Load: 1.288s, Pack+Encode: 7.990s, Decode+Unpack: 13.098s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 817.4039 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000536-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,336B, BPFP=0.2023 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 328,264B, BPFP=0.2041 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 810,664B, BPFP=0.5041 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 793,704B, BPFP=0.4935 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 788,668B, BPFP=0.4904 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 766,164B, BPFP=0.4764 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 451,356B, BPFP=0.2807 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 465,236B, BPFP=0.2893 +⌛️ [2/4] FRONTEND: Frontend time: 7.846s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.886s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 4.48069571 + layer.9.1 0.14239137 4.46695109 + layer.19.0 0.03888746 16.64425392 + layer.19.1 0.04246985 20.90466064 + layer.29.0 0.10356636 76.23704632 + layer.29.1 0.10009016 79.44075334 + layer.39.0 8.56607607 2393.91053805 + layer.39.1 7.91790657 2271.32632919 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 608.42640353 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4729392 +BPFP 0.3676 bits/point +EBPFP 0.3676 equivalent bits/point +MSE 608.426404 +---------------------- --------------------------------------------------------- +Time: 22.041s Load: 1.309s, Pack+Encode: 7.846s, Decode+Unpack: 12.886s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 608.4264 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000546-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,256B, BPFP=0.1886 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 308,556B, BPFP=0.1919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 645,636B, BPFP=0.4015 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 675,892B, BPFP=0.4203 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 677,356B, BPFP=0.4212 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 713,352B, BPFP=0.4436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,576B, BPFP=0.2777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 456,740B, BPFP=0.2840 +⌛️ [2/4] FRONTEND: Frontend time: 7.839s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.263s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 4.56289361 + layer.9.1 0.14243852 4.53669556 + layer.19.0 0.05701358 8.63216021 + layer.19.1 0.05730241 8.16110763 + layer.29.0 4.14713759 55.10075414 + layer.29.1 4.15440538 57.27094277 + layer.39.0 12.45677755 2337.14071952 + layer.39.1 14.71734096 2444.76774912 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 615.02162782 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4227364 +BPFP 0.3286 bits/point +EBPFP 0.3286 equivalent bits/point +MSE 615.021628 +---------------------- --------------------------------------------------------- +Time: 22.268s Load: 1.166s, Pack+Encode: 7.839s, Decode+Unpack: 13.263s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.0216 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000556-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,156B, BPFP=0.2041 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 324,528B, BPFP=0.2018 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 832,832B, BPFP=0.5179 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 828,024B, BPFP=0.5149 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,057,344B, BPFP=0.6575 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,022,468B, BPFP=0.6358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 677,208B, BPFP=0.4211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 658,244B, BPFP=0.4093 +⌛️ [2/4] FRONTEND: Frontend time: 7.438s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.123s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 4.60541652 + layer.9.1 0.11180697 4.60638375 + layer.19.0 0.09949989 26.45105062 + layer.19.1 0.11883939 12.39718019 + layer.29.0 0.15177689 305.56785259 + layer.29.1 0.14123031 379.95857211 + layer.39.0 349.58010984 3454.74243871 + layer.39.1 334.73010188 3420.42024833 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 951.09364285 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5728804 +BPFP 0.4453 bits/point +EBPFP 0.4453 equivalent bits/point +MSE 951.093643 +---------------------- --------------------------------------------------------- +Time: 21.808s Load: 1.247s, Pack+Encode: 7.438s, Decode+Unpack: 13.123s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 951.0936 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000620-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.315s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 285,824B, BPFP=0.1777 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 286,332B, BPFP=0.1780 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 520,392B, BPFP=0.3236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 495,884B, BPFP=0.3083 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 586,064B, BPFP=0.3644 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 553,256B, BPFP=0.3440 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,448B, BPFP=0.2670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 410,352B, BPFP=0.2552 +⌛️ [2/4] FRONTEND: Frontend time: 7.967s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.130s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72630507 4.70011914 + layer.9.1 2.71889861 4.71744232 + layer.19.0 3.15508441 10.10195238 + layer.19.1 3.14332772 10.38997209 + layer.29.0 4.15805451 57.12053287 + layer.29.1 4.14588961 66.74175223 + layer.39.0 8.22539970 2098.32632919 + layer.39.1 8.64785859 2228.63785419 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 560.09199430 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3567552 +BPFP 0.2773 bits/point +EBPFP 0.2773 equivalent bits/point +MSE 560.091994 +---------------------- --------------------------------------------------------- +Time: 22.412s Load: 1.315s, Pack+Encode: 7.967s, Decode+Unpack: 13.130s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 560.0920 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000624-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 320,392B, BPFP=0.1992 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 332,372B, BPFP=0.2067 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 806,868B, BPFP=0.5017 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 830,028B, BPFP=0.5161 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 802,900B, BPFP=0.4993 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 840,240B, BPFP=0.5225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 518,884B, BPFP=0.3227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 551,912B, BPFP=0.3432 +⌛️ [2/4] FRONTEND: Frontend time: 7.910s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.124s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 4.56946403 + layer.9.1 0.11119189 4.57285480 + layer.19.0 0.08174444 7.78502940 + layer.19.1 0.08249469 16.47272465 + layer.29.0 4.18188438 131.05187440 + layer.29.1 4.20908200 99.97428168 + layer.39.0 9.33443395 2611.86453359 + layer.39.1 9.53268950 2957.77777778 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 729.25856754 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5003596 +BPFP 0.3889 bits/point +EBPFP 0.3889 equivalent bits/point +MSE 729.258568 +---------------------- --------------------------------------------------------- +Time: 22.345s Load: 1.311s, Pack+Encode: 7.910s, Decode+Unpack: 13.124s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 729.2586 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 317,108B, BPFP=0.1972 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 323,468B, BPFP=0.2011 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 728,044B, BPFP=0.4527 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 722,944B, BPFP=0.4495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 698,660B, BPFP=0.4344 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 708,276B, BPFP=0.4404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 470,572B, BPFP=0.2926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 475,524B, BPFP=0.2957 +⌛️ [2/4] FRONTEND: Frontend time: 7.978s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.093s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 4.44789459 + layer.9.1 0.03285184 4.45263159 + layer.19.0 0.04037820 7.65388088 + layer.19.1 0.04362713 7.58343531 + layer.29.0 0.11518513 99.81823066 + layer.29.1 0.11703357 73.10514167 + layer.39.0 256.78569723 2539.52419612 + layer.39.1 143.16752229 2417.08277619 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 644.20852338 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4444596 +BPFP 0.3455 bits/point +EBPFP 0.3455 equivalent bits/point +MSE 644.208523 +---------------------- --------------------------------------------------------- +Time: 22.381s Load: 1.311s, Pack+Encode: 7.978s, Decode+Unpack: 13.093s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 644.2085 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000676-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 321,720B, BPFP=0.2001 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 317,848B, BPFP=0.1976 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 678,228B, BPFP=0.4217 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 673,732B, BPFP=0.4189 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 723,368B, BPFP=0.4498 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 723,652B, BPFP=0.4500 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,240B, BPFP=0.2905 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 448,592B, BPFP=0.2789 +⌛️ [2/4] FRONTEND: Frontend time: 7.913s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.702s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 4.54802007 + layer.9.1 0.11256296 4.54752915 + layer.19.0 0.03396921 17.06067310 + layer.19.1 0.04105656 12.10427859 + layer.29.0 4.20373127 94.45566699 + layer.29.1 4.19418701 67.90094218 + layer.39.0 8.83613586 2215.54154728 + layer.39.1 8.48765384 2188.68815664 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 575.60585175 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4354380 +BPFP 0.3385 bits/point +EBPFP 0.3385 equivalent bits/point +MSE 575.605852 +---------------------- --------------------------------------------------------- +Time: 21.920s Load: 1.306s, Pack+Encode: 7.913s, Decode+Unpack: 12.702s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 575.6059 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000678-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,364B, BPFP=0.2060 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 342,588B, BPFP=0.2130 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 822,420B, BPFP=0.5114 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 774,148B, BPFP=0.4814 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 844,336B, BPFP=0.5250 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 842,072B, BPFP=0.5236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 548,008B, BPFP=0.3408 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 549,464B, BPFP=0.3417 +⌛️ [2/4] FRONTEND: Frontend time: 7.903s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.993s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 4.57339236 + layer.9.1 0.03228644 4.51082240 + layer.19.0 0.12067159 44.51965934 + layer.19.1 0.11791951 21.50510884 + layer.29.0 0.15835167 156.34931152 + layer.29.1 0.15268422 168.48046004 + layer.39.0 158.29335801 2960.84177014 + layer.39.1 131.92238738 3017.45654250 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 797.27963339 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5054400 +BPFP 0.3929 bits/point +EBPFP 0.3929 equivalent bits/point +MSE 797.279633 +---------------------- --------------------------------------------------------- +Time: 22.202s Load: 1.306s, Pack+Encode: 7.903s, Decode+Unpack: 12.993s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 797.2796 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000684-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 304,076B, BPFP=0.1891 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 311,572B, BPFP=0.1937 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 658,336B, BPFP=0.4094 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 684,728B, BPFP=0.4258 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 762,184B, BPFP=0.4739 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 813,792B, BPFP=0.5060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,112B, BPFP=0.2954 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 499,832B, BPFP=0.3108 +⌛️ [2/4] FRONTEND: Frontend time: 7.993s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.163s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 4.55688966 + layer.9.1 0.03230341 4.52351616 + layer.19.0 0.01113602 21.50938196 + layer.19.1 0.03747142 7.71477659 + layer.29.0 4.12172023 76.46175581 + layer.29.1 4.13913264 61.68397803 + layer.39.0 9.31610902 1983.07020057 + layer.39.1 11.00762596 2095.16762178 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 531.83601507 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4509632 +BPFP 0.3505 bits/point +EBPFP 0.3505 equivalent bits/point +MSE 531.836015 +---------------------- --------------------------------------------------------- +Time: 22.468s Load: 1.312s, Pack+Encode: 7.993s, Decode+Unpack: 13.163s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 531.8360 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000693-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.314s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,040B, BPFP=0.2058 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 320,168B, BPFP=0.1991 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 787,728B, BPFP=0.4898 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 791,204B, BPFP=0.4920 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 851,784B, BPFP=0.5297 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 834,004B, BPFP=0.5186 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 540,360B, BPFP=0.3360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 542,724B, BPFP=0.3375 +⌛️ [2/4] FRONTEND: Frontend time: 7.957s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.956s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14187056 4.50261132 + layer.9.1 0.14241365 4.54210909 + layer.19.0 0.11657135 48.73515600 + layer.19.1 0.11473399 30.79659891 + layer.29.0 0.16421308 127.73300700 + layer.29.1 0.18111406 156.62316937 + layer.39.0 55.30549089 3005.51289398 + layer.39.1 49.87731316 3036.48519580 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 801.86634268 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4999012 +BPFP 0.3886 bits/point +EBPFP 0.3886 equivalent bits/point +MSE 801.866343 +---------------------- --------------------------------------------------------- +Time: 22.228s Load: 1.314s, Pack+Encode: 7.957s, Decode+Unpack: 12.956s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 801.8663 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000713-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.263s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 304,676B, BPFP=0.1895 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 299,492B, BPFP=0.1862 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 630,040B, BPFP=0.3918 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 686,380B, BPFP=0.4268 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 742,160B, BPFP=0.4615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 720,316B, BPFP=0.4479 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 502,040B, BPFP=0.3122 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 486,068B, BPFP=0.3022 +⌛️ [2/4] FRONTEND: Frontend time: 7.926s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 4.51750475 + layer.9.1 0.03232725 4.52808029 + layer.19.0 0.03714494 7.59249144 + layer.19.1 0.03685654 7.41103674 + layer.29.0 4.16145554 53.61268804 + layer.29.1 4.17130075 58.49883099 + layer.39.0 7.63807493 2240.03295129 + layer.39.1 7.26751532 2070.66969118 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 555.85790934 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4371172 +BPFP 0.3398 bits/point +EBPFP 0.3398 equivalent bits/point +MSE 555.857909 +---------------------- --------------------------------------------------------- +Time: 22.490s Load: 1.263s, Pack+Encode: 7.926s, Decode+Unpack: 13.301s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 555.8579 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000734-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.315s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,008B, BPFP=0.1884 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 310,724B, BPFP=0.1932 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 627,304B, BPFP=0.3901 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 636,428B, BPFP=0.3957 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 713,184B, BPFP=0.4435 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 740,908B, BPFP=0.4607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,408B, BPFP=0.2882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 465,064B, BPFP=0.2892 +⌛️ [2/4] FRONTEND: Frontend time: 8.016s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.085s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 4.50006405 + layer.9.1 0.14394252 4.48976708 + layer.19.0 0.03713998 7.78714855 + layer.19.1 0.11359857 21.43676874 + layer.29.0 4.20669858 67.36461318 + layer.29.1 0.11083615 89.80895813 + layer.39.0 7.41086201 2272.77554919 + layer.39.1 8.74303628 2493.09869468 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 620.15769545 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4260028 +BPFP 0.3311 bits/point +EBPFP 0.3311 equivalent bits/point +MSE 620.157695 +---------------------- --------------------------------------------------------- +Time: 22.416s Load: 1.315s, Pack+Encode: 8.016s, Decode+Unpack: 13.085s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 620.1577 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000737-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 321,072B, BPFP=0.1996 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 316,864B, BPFP=0.1970 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 770,124B, BPFP=0.4789 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 784,044B, BPFP=0.4875 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 987,872B, BPFP=0.6143 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,050,188B, BPFP=0.6530 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 527,452B, BPFP=0.3280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 541,804B, BPFP=0.3369 +⌛️ [2/4] FRONTEND: Frontend time: 7.969s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.094s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 4.49579217 + layer.9.1 0.14198353 4.51053979 + layer.19.0 0.17418623 26.10620125 + layer.19.1 0.18921874 39.52116911 + layer.29.0 0.15243895 199.92331264 + layer.29.1 0.17994503 291.01784862 + layer.39.0 13.57905399 2748.83349252 + layer.39.1 8.80701993 2846.09614772 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 770.06306298 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5299420 +BPFP 0.4119 bits/point +EBPFP 0.4119 equivalent bits/point +MSE 770.063063 +---------------------- --------------------------------------------------------- +Time: 22.373s Load: 1.311s, Pack+Encode: 7.969s, Decode+Unpack: 13.094s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 770.0631 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000804-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 318,404B, BPFP=0.1980 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 313,296B, BPFP=0.1948 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 784,376B, BPFP=0.4877 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 800,688B, BPFP=0.4979 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 987,424B, BPFP=0.6140 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 954,340B, BPFP=0.5934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 528,508B, BPFP=0.3286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 507,500B, BPFP=0.3156 +⌛️ [2/4] FRONTEND: Frontend time: 7.861s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72336357 4.60780989 + layer.9.1 2.61637510 4.61588666 + layer.19.0 0.14860626 153.18196832 + layer.19.1 0.15499876 72.63883417 + layer.29.0 0.29089499 300.15618036 + layer.29.1 0.20993857 264.99007084 + layer.39.0 12.63850088 2999.38172557 + layer.39.1 9.97545753 2879.08946195 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 834.83274222 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5194536 +BPFP 0.4038 bits/point +EBPFP 0.4038 equivalent bits/point +MSE 834.832742 +---------------------- --------------------------------------------------------- +Time: 22.207s Load: 1.305s, Pack+Encode: 7.861s, Decode+Unpack: 13.041s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 834.8327 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000816-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 320,572B, BPFP=0.1993 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 332,296B, BPFP=0.2066 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 811,852B, BPFP=0.5048 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 818,388B, BPFP=0.5089 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,071,480B, BPFP=0.6663 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,030,612B, BPFP=0.6409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 550,356B, BPFP=0.3422 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 537,756B, BPFP=0.3344 +⌛️ [2/4] FRONTEND: Frontend time: 7.957s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.145s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 4.51684407 + layer.9.1 0.14187655 4.48525705 + layer.19.0 0.17405892 26.35700961 + layer.19.1 0.14315577 35.54431312 + layer.29.0 0.19218995 472.42470551 + layer.29.1 0.16272765 263.07871697 + layer.39.0 14.01399584 2844.19929959 + layer.39.1 9.48776763 2712.40751353 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 795.37670743 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5473312 +BPFP 0.4254 bits/point +EBPFP 0.4254 equivalent bits/point +MSE 795.376707 +---------------------- --------------------------------------------------------- +Time: 22.409s Load: 1.306s, Pack+Encode: 7.957s, Decode+Unpack: 13.145s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 795.3767 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000817-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.263s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 333,632B, BPFP=0.2075 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 322,780B, BPFP=0.2007 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 774,440B, BPFP=0.4816 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 714,404B, BPFP=0.4442 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 863,404B, BPFP=0.5369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 800,612B, BPFP=0.4978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 528,540B, BPFP=0.3287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 517,984B, BPFP=0.3221 +⌛️ [2/4] FRONTEND: Frontend time: 7.958s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.903s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 4.51144950 + layer.9.1 0.14252999 4.53087256 + layer.19.0 0.12443910 35.61595133 + layer.19.1 0.13256963 30.52290523 + layer.29.0 4.20758094 63.05640620 + layer.29.1 4.18155761 52.14017729 + layer.39.0 45.67507362 2673.89907673 + layer.39.1 52.99942295 2572.00795925 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 679.53559976 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4855796 +BPFP 0.3774 bits/point +EBPFP 0.3774 equivalent bits/point +MSE 679.535600 +---------------------- --------------------------------------------------------- +Time: 22.124s Load: 1.263s, Pack+Encode: 7.958s, Decode+Unpack: 12.903s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 679.5356 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000880-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.341s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,652B, BPFP=0.2031 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 327,436B, BPFP=0.2036 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 743,864B, BPFP=0.4625 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 761,344B, BPFP=0.4734 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 861,048B, BPFP=0.5354 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 859,032B, BPFP=0.5342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 527,652B, BPFP=0.3281 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 513,608B, BPFP=0.3194 +⌛️ [2/4] FRONTEND: Frontend time: 7.922s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.113s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 4.61300516 + layer.9.1 0.14194541 4.62371844 + layer.19.0 0.11782019 35.99131944 + layer.19.1 0.12099331 33.11694126 + layer.29.0 0.31534543 421.35390799 + layer.29.1 0.31351768 251.19957816 + layer.39.0 16.41217467 2646.21887934 + layer.39.1 11.15875965 2831.87647246 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 778.62422778 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4920636 +BPFP 0.3825 bits/point +EBPFP 0.3825 equivalent bits/point +MSE 778.624228 +---------------------- --------------------------------------------------------- +Time: 22.376s Load: 1.341s, Pack+Encode: 7.922s, Decode+Unpack: 13.113s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 778.6242 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.307s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 320,468B, BPFP=0.1993 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 316,040B, BPFP=0.1965 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 680,048B, BPFP=0.4229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 662,428B, BPFP=0.4119 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 857,848B, BPFP=0.5334 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 835,500B, BPFP=0.5195 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 517,856B, BPFP=0.3220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 499,448B, BPFP=0.3106 +⌛️ [2/4] FRONTEND: Frontend time: 7.952s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.204s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 4.51837592 + layer.9.1 0.14279503 4.56444784 + layer.19.0 0.04409784 12.43830712 + layer.19.1 0.12204415 22.03150868 + layer.29.0 0.14332971 152.95534862 + layer.29.1 0.16018698 85.85917104 + layer.39.0 8.52841700 2545.99363260 + layer.39.1 19.04729908 2562.57736390 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 673.86726946 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4689636 +BPFP 0.3645 bits/point +EBPFP 0.3645 equivalent bits/point +MSE 673.867269 +---------------------- --------------------------------------------------------- +Time: 22.462s Load: 1.307s, Pack+Encode: 7.952s, Decode+Unpack: 13.204s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 673.8673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000892-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 320,476B, BPFP=0.1993 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 314,328B, BPFP=0.1955 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 761,920B, BPFP=0.4738 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 787,320B, BPFP=0.4896 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 767,732B, BPFP=0.4774 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 752,104B, BPFP=0.4677 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 489,068B, BPFP=0.3041 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 480,776B, BPFP=0.2990 +⌛️ [2/4] FRONTEND: Frontend time: 7.923s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 4.52021245 + layer.9.1 0.03263012 4.49162382 + layer.19.0 0.05225635 12.24861584 + layer.19.1 0.04916960 16.72060799 + layer.29.0 4.19413323 74.71612345 + layer.29.1 4.20728930 99.42737186 + layer.39.0 8.98594322 2236.88140720 + layer.39.1 8.30659896 2206.80292900 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 581.97611145 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4673724 +BPFP 0.3633 bits/point +EBPFP 0.3633 equivalent bits/point +MSE 581.976111 +---------------------- --------------------------------------------------------- +Time: 22.258s Load: 1.311s, Pack+Encode: 7.923s, Decode+Unpack: 13.024s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 581.9761 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000919-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 330,084B, BPFP=0.2053 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 322,648B, BPFP=0.2006 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 783,432B, BPFP=0.4872 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 753,748B, BPFP=0.4687 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 840,748B, BPFP=0.5228 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 830,148B, BPFP=0.5162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,972B, BPFP=0.3501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 562,012B, BPFP=0.3495 +⌛️ [2/4] FRONTEND: Frontend time: 7.843s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.164s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258133 4.54556079 + layer.9.1 0.03283905 4.53590212 + layer.19.0 0.03703246 25.89454244 + layer.19.1 0.03684524 17.05220397 + layer.29.0 0.11326863 50.84301377 + layer.29.1 0.10834243 118.49998010 + layer.39.0 11.60468402 2736.20757720 + layer.39.1 14.87000682 2417.87185610 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 671.93132956 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4985792 +BPFP 0.3875 bits/point +EBPFP 0.3875 equivalent bits/point +MSE 671.931330 +---------------------- --------------------------------------------------------- +Time: 22.318s Load: 1.310s, Pack+Encode: 7.843s, Decode+Unpack: 13.164s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 671.9313 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000925-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 314,584B, BPFP=0.1956 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 316,952B, BPFP=0.1971 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 754,564B, BPFP=0.4692 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 757,192B, BPFP=0.4708 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 905,248B, BPFP=0.5629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 914,452B, BPFP=0.5686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 548,596B, BPFP=0.3411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 554,348B, BPFP=0.3447 +⌛️ [2/4] FRONTEND: Frontend time: 7.805s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.094s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 4.66423412 + layer.9.1 0.11188250 4.61821350 + layer.19.0 3.25906142 26.02200235 + layer.19.1 3.26015426 21.71712333 + layer.29.0 4.19564952 181.36109121 + layer.29.1 4.21244012 147.15366324 + layer.39.0 303.99934336 3198.06749443 + layer.39.1 331.94728988 3231.77459408 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 851.92230203 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5065936 +BPFP 0.3938 bits/point +EBPFP 0.3938 equivalent bits/point +MSE 851.922302 +---------------------- --------------------------------------------------------- +Time: 22.216s Load: 1.317s, Pack+Encode: 7.805s, Decode+Unpack: 13.094s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 851.9223 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000927-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.318s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,580B, BPFP=0.1863 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 293,528B, BPFP=0.1825 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 705,540B, BPFP=0.4387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 657,188B, BPFP=0.4087 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 673,908B, BPFP=0.4190 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 660,716B, BPFP=0.4108 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 519,852B, BPFP=0.3233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 507,980B, BPFP=0.3159 +⌛️ [2/4] FRONTEND: Frontend time: 7.901s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.923s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 4.49845572 + layer.9.1 0.00271392 4.53341019 + layer.19.0 3.19073251 7.19232467 + layer.19.1 3.15044721 7.37975068 + layer.29.0 4.17151372 42.68512715 + layer.29.1 4.17302847 43.29687997 + layer.39.0 85.12206503 2368.21776504 + layer.39.1 85.43754975 2264.31120662 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 592.76436501 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4318292 +BPFP 0.3356 bits/point +EBPFP 0.3356 equivalent bits/point +MSE 592.764365 +---------------------- --------------------------------------------------------- +Time: 22.142s Load: 1.318s, Pack+Encode: 7.901s, Decode+Unpack: 12.923s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 592.7644 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000942-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 332,112B, BPFP=0.2065 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 329,904B, BPFP=0.2051 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 768,724B, BPFP=0.4780 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 745,532B, BPFP=0.4636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 926,076B, BPFP=0.5758 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 862,696B, BPFP=0.5364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 587,820B, BPFP=0.3655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 562,940B, BPFP=0.3500 +⌛️ [2/4] FRONTEND: Frontend time: 7.926s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 4.63723299 + layer.9.1 2.75948239 8.67656909 + layer.19.0 0.15224024 8.18263553 + layer.19.1 0.13045117 31.38845859 + layer.29.0 0.13097460 298.95692057 + layer.29.1 0.13177276 98.31605181 + layer.39.0 10.49186664 2964.77013690 + layer.39.1 12.55703299 3004.61031519 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 802.44229008 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5115804 +BPFP 0.3976 bits/point +EBPFP 0.3976 equivalent bits/point +MSE 802.442290 +---------------------- --------------------------------------------------------- +Time: 22.296s Load: 1.308s, Pack+Encode: 7.926s, Decode+Unpack: 13.062s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 802.4423 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000946-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.307s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 301,424B, BPFP=0.1874 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 299,904B, BPFP=0.1865 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 603,100B, BPFP=0.3750 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 637,188B, BPFP=0.3962 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 747,004B, BPFP=0.4645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 755,572B, BPFP=0.4698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 491,660B, BPFP=0.3057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 501,820B, BPFP=0.3120 +⌛️ [2/4] FRONTEND: Frontend time: 7.893s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.115s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 4.64535547 + layer.9.1 0.03228249 4.62034073 + layer.19.0 0.04154089 8.54105045 + layer.19.1 0.04120101 17.94143361 + layer.29.0 4.21417063 94.04051258 + layer.29.1 4.21428318 82.43864912 + layer.39.0 28.58093312 2500.18640560 + layer.39.1 17.10356972 2432.27347978 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 643.08590342 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4337672 +BPFP 0.3372 bits/point +EBPFP 0.3372 equivalent bits/point +MSE 643.085903 +---------------------- --------------------------------------------------------- +Time: 22.315s Load: 1.307s, Pack+Encode: 7.893s, Decode+Unpack: 13.115s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 643.0859 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000959-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 337,668B, BPFP=0.2100 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 333,984B, BPFP=0.2077 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 816,724B, BPFP=0.5079 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 813,400B, BPFP=0.5058 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 907,432B, BPFP=0.5643 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 905,152B, BPFP=0.5628 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 583,996B, BPFP=0.3631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 583,096B, BPFP=0.3626 +⌛️ [2/4] FRONTEND: Frontend time: 7.939s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.242s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 4.55326478 + layer.9.1 0.14242138 4.52941999 + layer.19.0 0.13512425 34.89057277 + layer.19.1 0.13152432 43.98761342 + layer.29.0 0.11439834 121.51258556 + layer.29.1 0.11806111 93.29543736 + layer.39.0 18.41482236 2787.77077364 + layer.39.1 20.38586935 2783.64342566 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 734.27288665 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5281452 +BPFP 0.4105 bits/point +EBPFP 0.4105 equivalent bits/point +MSE 734.272887 +---------------------- --------------------------------------------------------- +Time: 22.491s Load: 1.310s, Pack+Encode: 7.939s, Decode+Unpack: 13.242s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 734.2729 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000972-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 332,508B, BPFP=0.2068 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 316,404B, BPFP=0.1967 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 703,816B, BPFP=0.4376 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 656,208B, BPFP=0.4080 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 793,916B, BPFP=0.4937 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 738,284B, BPFP=0.4591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 546,436B, BPFP=0.3398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 500,352B, BPFP=0.3111 +⌛️ [2/4] FRONTEND: Frontend time: 7.886s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.663s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 4.53462833 + layer.9.1 0.14251336 4.48023184 + layer.19.0 0.11881898 36.41091362 + layer.19.1 0.11371834 35.78263541 + layer.29.0 0.15377442 131.41564191 + layer.29.1 0.16319071 150.00821792 + layer.39.0 9.10150218 2667.21537727 + layer.39.1 9.15265777 2570.51544094 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 700.04538591 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4587924 +BPFP 0.3566 bits/point +EBPFP 0.3566 equivalent bits/point +MSE 700.045386 +---------------------- --------------------------------------------------------- +Time: 21.853s Load: 1.304s, Pack+Encode: 7.886s, Decode+Unpack: 12.663s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 700.0454 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001000-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 308,020B, BPFP=0.1915 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 306,172B, BPFP=0.1904 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 562,956B, BPFP=0.3501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 576,768B, BPFP=0.3586 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 691,612B, BPFP=0.4301 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 659,788B, BPFP=0.4103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 506,952B, BPFP=0.3152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 501,060B, BPFP=0.3116 +⌛️ [2/4] FRONTEND: Frontend time: 7.913s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 4.63305066 + layer.9.1 0.14223260 4.62524344 + layer.19.0 0.05715554 13.81701066 + layer.19.1 0.06015340 31.34875438 + layer.29.0 0.19165729 114.56081861 + layer.29.1 0.21090307 122.02728032 + layer.39.0 19.07211701 2678.15027061 + layer.39.1 16.66110887 2797.83508437 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 720.87468913 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4113328 +BPFP 0.3197 bits/point +EBPFP 0.3197 equivalent bits/point +MSE 720.874689 +---------------------- --------------------------------------------------------- +Time: 22.224s Load: 1.305s, Pack+Encode: 7.913s, Decode+Unpack: 13.006s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 720.8747 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 338,680B, BPFP=0.2106 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 336,096B, BPFP=0.2090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 849,648B, BPFP=0.5283 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 815,056B, BPFP=0.5068 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 923,580B, BPFP=0.5743 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 883,344B, BPFP=0.5493 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 615,832B, BPFP=0.3829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 576,444B, BPFP=0.3584 +⌛️ [2/4] FRONTEND: Frontend time: 7.992s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.226s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 4.54729193 + layer.9.1 0.14288678 4.55067243 + layer.19.0 0.11144568 16.53224491 + layer.19.1 0.11742487 7.27849100 + layer.29.0 0.11418290 189.09626711 + layer.29.1 0.10734091 75.01470969 + layer.39.0 54.48020137 3446.86723973 + layer.39.1 66.40954314 3081.51926138 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 853.17577227 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5338680 +BPFP 0.4150 bits/point +EBPFP 0.4150 equivalent bits/point +MSE 853.175772 +---------------------- --------------------------------------------------------- +Time: 22.526s Load: 1.308s, Pack+Encode: 7.992s, Decode+Unpack: 13.226s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 853.1758 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001056-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.259s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,092B, BPFP=0.1885 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 304,420B, BPFP=0.1893 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 670,360B, BPFP=0.4168 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 625,800B, BPFP=0.3891 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 761,696B, BPFP=0.4736 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 730,024B, BPFP=0.4539 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 551,548B, BPFP=0.3430 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 537,108B, BPFP=0.3340 +⌛️ [2/4] FRONTEND: Frontend time: 7.888s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.103s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 4.66431496 + layer.9.1 0.00081411 4.66630104 + layer.19.0 0.01015774 13.16708701 + layer.19.1 3.16362350 8.19877850 + layer.29.0 4.19769406 82.40613061 + layer.29.1 4.18061463 62.80788364 + layer.39.0 8.41366640 2662.23447947 + layer.39.1 8.38033145 2746.46768545 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 698.07658258 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4484048 +BPFP 0.3485 bits/point +EBPFP 0.3485 equivalent bits/point +MSE 698.076583 +---------------------- --------------------------------------------------------- +Time: 22.250s Load: 1.259s, Pack+Encode: 7.888s, Decode+Unpack: 13.103s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 698.0766 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001074-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.338s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 287,884B, BPFP=0.1790 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 296,744B, BPFP=0.1845 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 647,592B, BPFP=0.4027 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 687,416B, BPFP=0.4274 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 816,544B, BPFP=0.5077 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 807,364B, BPFP=0.5020 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 552,576B, BPFP=0.3436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 555,556B, BPFP=0.3455 +⌛️ [2/4] FRONTEND: Frontend time: 7.950s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.052s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 4.65641043 + layer.9.1 0.03271215 4.63890506 + layer.19.0 3.19210144 8.36363631 + layer.19.1 3.19171965 21.67143227 + layer.29.0 0.11530653 84.34465536 + layer.29.1 0.10966549 82.46161652 + layer.39.0 16.12381606 2391.10585801 + layer.39.1 25.33235335 2656.81757402 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 656.75751100 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4651676 +BPFP 0.3616 bits/point +EBPFP 0.3616 equivalent bits/point +MSE 656.757511 +---------------------- --------------------------------------------------------- +Time: 22.341s Load: 1.338s, Pack+Encode: 7.950s, Decode+Unpack: 13.052s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 656.7575 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001078-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.321s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 301,888B, BPFP=0.1877 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 304,500B, BPFP=0.1893 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 608,792B, BPFP=0.3786 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 723,208B, BPFP=0.4497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 810,832B, BPFP=0.5042 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 920,236B, BPFP=0.5722 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 540,548B, BPFP=0.3361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 567,108B, BPFP=0.3526 +⌛️ [2/4] FRONTEND: Frontend time: 7.946s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.109s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64207787 4.68074397 + layer.9.1 0.03100527 4.65009806 + layer.19.0 3.19321449 12.37526738 + layer.19.1 3.20089330 17.05910737 + layer.29.0 0.10652387 110.17777977 + layer.29.1 0.17364564 228.40450891 + layer.39.0 9.89558772 2575.42406877 + layer.39.1 12.87769495 3153.71983445 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 763.31142608 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4777112 +BPFP 0.3713 bits/point +EBPFP 0.3713 equivalent bits/point +MSE 763.311426 +---------------------- --------------------------------------------------------- +Time: 22.375s Load: 1.321s, Pack+Encode: 7.946s, Decode+Unpack: 13.109s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 763.3114 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001086-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 306,068B, BPFP=0.1903 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 304,880B, BPFP=0.1896 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 694,564B, BPFP=0.4319 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 657,560B, BPFP=0.4089 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 803,372B, BPFP=0.4995 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 812,160B, BPFP=0.5050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 582,904B, BPFP=0.3625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 581,072B, BPFP=0.3613 +⌛️ [2/4] FRONTEND: Frontend time: 7.918s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.060s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 4.57519718 + layer.9.1 0.03183258 4.60165950 + layer.19.0 0.03873757 7.74739024 + layer.19.1 0.03841183 16.93044736 + layer.29.0 0.10242378 86.28500080 + layer.29.1 0.10979955 202.35382840 + layer.39.0 11.55027136 2823.32123528 + layer.39.1 12.74680635 3001.95224451 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 768.47087541 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4742580 +BPFP 0.3686 bits/point +EBPFP 0.3686 equivalent bits/point +MSE 768.470875 +---------------------- --------------------------------------------------------- +Time: 22.293s Load: 1.316s, Pack+Encode: 7.918s, Decode+Unpack: 13.060s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 768.4709 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001102-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 310,952B, BPFP=0.1934 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 318,668B, BPFP=0.1982 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 788,444B, BPFP=0.4903 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 809,940B, BPFP=0.5036 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 867,748B, BPFP=0.5396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 904,528B, BPFP=0.5625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 571,736B, BPFP=0.3555 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 606,340B, BPFP=0.3770 +⌛️ [2/4] FRONTEND: Frontend time: 7.977s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.042s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 4.53255364 + layer.9.1 0.03112686 4.57109288 + layer.19.0 0.03695946 7.85868105 + layer.19.1 0.03932408 12.28092044 + layer.29.0 0.11080087 147.97918656 + layer.29.1 0.12351766 160.34407832 + layer.39.0 27.63217079 2822.66189112 + layer.39.1 35.42625259 2909.97484877 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 758.77540660 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5178356 +BPFP 0.4025 bits/point +EBPFP 0.4025 equivalent bits/point +MSE 758.775407 +---------------------- --------------------------------------------------------- +Time: 22.329s Load: 1.310s, Pack+Encode: 7.977s, Decode+Unpack: 13.042s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 758.7754 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001107-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,356B, BPFP=0.1961 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 311,364B, BPFP=0.1936 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 750,408B, BPFP=0.4666 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 733,976B, BPFP=0.4564 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 863,912B, BPFP=0.5372 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 839,784B, BPFP=0.5222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 565,560B, BPFP=0.3517 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 559,760B, BPFP=0.3481 +⌛️ [2/4] FRONTEND: Frontend time: 7.869s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.914s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 4.56160987 + layer.9.1 0.11126176 4.59113775 + layer.19.0 0.00622823 7.43987596 + layer.19.1 0.00986777 16.55761750 + layer.29.0 4.20227933 168.33669612 + layer.29.1 4.19170939 113.04146769 + layer.39.0 64.89367936 3002.65902579 + layer.39.1 48.85537050 2848.16173193 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 770.66864532 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4940120 +BPFP 0.3840 bits/point +EBPFP 0.3840 equivalent bits/point +MSE 770.668645 +---------------------- --------------------------------------------------------- +Time: 22.072s Load: 1.289s, Pack+Encode: 7.869s, Decode+Unpack: 12.914s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 770.6686 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001116-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,388B, BPFP=0.1924 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 308,476B, BPFP=0.1918 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 713,936B, BPFP=0.4439 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 736,352B, BPFP=0.4579 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 833,008B, BPFP=0.5180 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 846,296B, BPFP=0.5262 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 535,772B, BPFP=0.3332 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 542,280B, BPFP=0.3372 +⌛️ [2/4] FRONTEND: Frontend time: 7.972s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 4.59556166 + layer.9.1 0.03110840 4.61699225 + layer.19.0 0.11193399 8.32084851 + layer.19.1 0.11167925 7.43752052 + layer.29.0 0.13638519 118.22545567 + layer.29.1 0.13233996 90.92700374 + layer.39.0 10.36537055 2666.44126074 + layer.39.1 10.25938570 2614.42470551 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 689.37366857 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4825508 +BPFP 0.3751 bits/point +EBPFP 0.3751 equivalent bits/point +MSE 689.373669 +---------------------- --------------------------------------------------------- +Time: 22.342s Load: 1.308s, Pack+Encode: 7.972s, Decode+Unpack: 13.061s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 689.3737 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001125-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,332B, BPFP=0.2017 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 314,276B, BPFP=0.1954 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 716,532B, BPFP=0.4456 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 746,956B, BPFP=0.4645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 792,432B, BPFP=0.4927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 813,516B, BPFP=0.5059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 554,332B, BPFP=0.3447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 560,816B, BPFP=0.3487 +⌛️ [2/4] FRONTEND: Frontend time: 7.656s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.907s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14239891 4.55766413 + layer.9.1 0.14185137 4.50859848 + layer.19.0 0.03937967 7.49262153 + layer.19.1 0.04081462 12.13557212 + layer.29.0 4.18784542 115.65250915 + layer.29.1 4.19318340 66.18978331 + layer.39.0 9.46241929 2778.29926775 + layer.39.1 9.25020271 2793.79624323 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 722.82903246 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4823192 +BPFP 0.3749 bits/point +EBPFP 0.3749 equivalent bits/point +MSE 722.829032 +---------------------- --------------------------------------------------------- +Time: 21.872s Load: 1.309s, Pack+Encode: 7.656s, Decode+Unpack: 12.907s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 722.8290 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001139-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 302,820B, BPFP=0.1883 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 306,548B, BPFP=0.1906 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 684,824B, BPFP=0.4258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 689,144B, BPFP=0.4285 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 803,644B, BPFP=0.4997 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 808,988B, BPFP=0.5030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,996B, BPFP=0.3177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 512,208B, BPFP=0.3185 +⌛️ [2/4] FRONTEND: Frontend time: 7.965s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.091s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 4.49637761 + layer.9.1 0.14180939 4.51938201 + layer.19.0 0.04123239 8.72017023 + layer.19.1 0.03889530 17.23898142 + layer.29.0 0.17016378 93.53621458 + layer.29.1 0.15026704 103.18612703 + layer.39.0 12.11620503 2636.15902579 + layer.39.1 10.53236554 2668.97039160 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 692.10333378 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4619172 +BPFP 0.3590 bits/point +EBPFP 0.3590 equivalent bits/point +MSE 692.103334 +---------------------- --------------------------------------------------------- +Time: 22.371s Load: 1.316s, Pack+Encode: 7.965s, Decode+Unpack: 13.091s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 692.1033 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001145-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,452B, BPFP=0.1962 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 306,872B, BPFP=0.1908 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 756,400B, BPFP=0.4703 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 703,212B, BPFP=0.4373 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 875,172B, BPFP=0.5442 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 845,724B, BPFP=0.5259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 538,172B, BPFP=0.3346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 540,316B, BPFP=0.3360 +⌛️ [2/4] FRONTEND: Frontend time: 7.939s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.017s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 4.57627261 + layer.9.1 0.11141965 4.58644925 + layer.19.0 0.02960617 35.01562003 + layer.19.1 0.09893673 13.09563162 + layer.29.0 0.11288278 73.21967526 + layer.29.1 0.12156463 71.98895654 + layer.39.0 13.31952528 2753.01687361 + layer.39.1 8.92088009 2742.33906399 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 712.22981786 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4881320 +BPFP 0.3794 bits/point +EBPFP 0.3794 equivalent bits/point +MSE 712.229818 +---------------------- --------------------------------------------------------- +Time: 22.260s Load: 1.304s, Pack+Encode: 7.939s, Decode+Unpack: 13.017s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 712.2298 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001171-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,604B, BPFP=0.2031 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 320,048B, BPFP=0.1990 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 756,644B, BPFP=0.4705 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 797,896B, BPFP=0.4961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 923,660B, BPFP=0.5743 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 903,712B, BPFP=0.5619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,968B, BPFP=0.3569 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 541,292B, BPFP=0.3366 +⌛️ [2/4] FRONTEND: Frontend time: 7.968s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.299s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 4.61123734 + layer.9.1 0.03269095 4.62151006 + layer.19.0 0.03939078 16.77339895 + layer.19.1 0.03751187 21.27916567 + layer.29.0 0.14354374 132.74532394 + layer.29.1 0.12315212 88.51630651 + layer.39.0 10.67588198 2805.93155046 + layer.39.1 12.04857131 2865.26806749 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 742.46832005 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5143824 +BPFP 0.3998 bits/point +EBPFP 0.3998 equivalent bits/point +MSE 742.468320 +---------------------- --------------------------------------------------------- +Time: 22.578s Load: 1.310s, Pack+Encode: 7.968s, Decode+Unpack: 13.299s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 742.4683 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001179-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,316B, BPFP=0.1942 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 306,424B, BPFP=0.1905 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 732,708B, BPFP=0.4556 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 748,576B, BPFP=0.4655 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 856,056B, BPFP=0.5323 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 867,928B, BPFP=0.5397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 539,944B, BPFP=0.3357 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 537,068B, BPFP=0.3340 +⌛️ [2/4] FRONTEND: Frontend time: 7.969s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.138s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 4.60185724 + layer.9.1 0.03246013 4.61378772 + layer.19.0 0.05054442 30.71322081 + layer.19.1 0.04990058 22.03295378 + layer.29.0 4.26185866 127.01623687 + layer.29.1 4.26378007 148.59657553 + layer.39.0 11.04594849 2598.50111429 + layer.39.1 9.19037403 2446.99379179 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 672.88369225 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4901020 +BPFP 0.3809 bits/point +EBPFP 0.3809 equivalent bits/point +MSE 672.883692 +---------------------- --------------------------------------------------------- +Time: 22.414s Load: 1.306s, Pack+Encode: 7.969s, Decode+Unpack: 13.138s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 672.8837 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001184-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.323s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,892B, BPFP=0.2045 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 328,456B, BPFP=0.2042 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 754,912B, BPFP=0.4694 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 696,332B, BPFP=0.4330 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 857,028B, BPFP=0.5329 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 801,240B, BPFP=0.4982 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 556,504B, BPFP=0.3460 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 548,168B, BPFP=0.3409 +⌛️ [2/4] FRONTEND: Frontend time: 7.911s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.101s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 4.52942590 + layer.9.1 0.14317998 4.61652060 + layer.19.0 0.15093802 57.62497513 + layer.19.1 0.13472426 8.25302265 + layer.29.0 0.10723148 122.43868394 + layer.29.1 0.10832139 82.82486668 + layer.39.0 40.62415433 3150.48646928 + layer.39.1 9.85226018 3205.58611907 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 829.54501041 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4871532 +BPFP 0.3786 bits/point +EBPFP 0.3786 equivalent bits/point +MSE 829.545010 +---------------------- --------------------------------------------------------- +Time: 22.335s Load: 1.323s, Pack+Encode: 7.911s, Decode+Unpack: 13.101s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 829.5450 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001198-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,072B, BPFP=0.2015 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 316,576B, BPFP=0.1969 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 654,360B, BPFP=0.4069 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 697,148B, BPFP=0.4335 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 828,208B, BPFP=0.5150 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 855,676B, BPFP=0.5321 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,624B, BPFP=0.3530 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 593,736B, BPFP=0.3692 +⌛️ [2/4] FRONTEND: Frontend time: 7.948s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.135s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 4.59797400 + layer.9.1 0.03106517 4.60012362 + layer.19.0 0.04795660 30.56867588 + layer.19.1 0.11462555 39.97805237 + layer.29.0 4.19919699 146.75383039 + layer.29.1 4.19569772 171.06409185 + layer.39.0 34.63583701 2894.55619230 + layer.39.1 33.06685271 2941.62846227 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 779.21842533 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4837400 +BPFP 0.3760 bits/point +EBPFP 0.3760 equivalent bits/point +MSE 779.218425 +---------------------- --------------------------------------------------------- +Time: 22.394s Load: 1.311s, Pack+Encode: 7.948s, Decode+Unpack: 13.135s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 779.2184 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001272-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 318,268B, BPFP=0.1979 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 317,060B, BPFP=0.1972 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 774,664B, BPFP=0.4817 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 800,668B, BPFP=0.4979 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 973,400B, BPFP=0.6053 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 971,628B, BPFP=0.6042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,688B, BPFP=0.3406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 577,848B, BPFP=0.3593 +⌛️ [2/4] FRONTEND: Frontend time: 7.962s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 4.54210038 + layer.9.1 0.14287666 4.49957468 + layer.19.0 0.11209038 17.23826135 + layer.19.1 0.11164490 7.43920937 + layer.29.0 0.12578187 142.26538125 + layer.29.1 0.11401374 184.15988141 + layer.39.0 22.42121339 3179.10378860 + layer.39.1 25.87191330 3240.16300541 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 847.42640031 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5281224 +BPFP 0.4105 bits/point +EBPFP 0.4105 equivalent bits/point +MSE 847.426400 +---------------------- --------------------------------------------------------- +Time: 22.262s Load: 1.311s, Pack+Encode: 7.962s, Decode+Unpack: 12.988s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 847.4264 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001342-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 306,784B, BPFP=0.1908 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 312,340B, BPFP=0.1942 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 685,572B, BPFP=0.4263 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 732,304B, BPFP=0.4554 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 765,508B, BPFP=0.4760 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 821,616B, BPFP=0.5109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 523,612B, BPFP=0.3256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 539,912B, BPFP=0.3357 +⌛️ [2/4] FRONTEND: Frontend time: 7.933s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.225s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 4.59144057 + layer.9.1 0.00120738 4.59557317 + layer.19.0 0.01953576 21.89679093 + layer.19.1 0.08568942 21.43958184 + layer.29.0 0.14491542 187.29210443 + layer.29.1 0.15694472 166.12133875 + layer.39.0 8.88920166 2682.60872334 + layer.39.1 9.38273353 2801.91595033 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 736.30768792 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4687648 +BPFP 0.3644 bits/point +EBPFP 0.3644 equivalent bits/point +MSE 736.307688 +---------------------- --------------------------------------------------------- +Time: 22.463s Load: 1.306s, Pack+Encode: 7.933s, Decode+Unpack: 13.225s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 736.3077 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001421-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 356,808B, BPFP=0.2219 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 351,072B, BPFP=0.2183 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 783,980B, BPFP=0.4875 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 772,444B, BPFP=0.4803 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 979,004B, BPFP=0.6088 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 942,020B, BPFP=0.5858 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,908B, BPFP=0.3202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 528,184B, BPFP=0.3284 +⌛️ [2/4] FRONTEND: Frontend time: 7.985s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.170s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 4.71062535 + layer.9.1 0.14739036 4.59396422 + layer.19.0 0.16044666 95.78088188 + layer.19.1 0.14398357 172.29944683 + layer.29.0 0.50679369 435.98296721 + layer.29.1 0.43405572 324.17267590 + layer.39.0 123.83094556 2698.78382681 + layer.39.1 72.08861628 2602.15982171 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 792.31052624 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5228420 +BPFP 0.4064 bits/point +EBPFP 0.4064 equivalent bits/point +MSE 792.310526 +---------------------- --------------------------------------------------------- +Time: 22.460s Load: 1.304s, Pack+Encode: 7.985s, Decode+Unpack: 13.170s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 792.3105 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001428-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.307s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 307,024B, BPFP=0.1909 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 317,552B, BPFP=0.1975 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 607,956B, BPFP=0.3780 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 613,304B, BPFP=0.3814 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 627,036B, BPFP=0.3899 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 636,932B, BPFP=0.3961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,700B, BPFP=0.2914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 488,472B, BPFP=0.3037 +⌛️ [2/4] FRONTEND: Frontend time: 7.882s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.126s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 4.50262935 + layer.9.1 0.14229169 4.54882471 + layer.19.0 0.04567823 26.47245354 + layer.19.1 0.04432558 12.52136063 + layer.29.0 0.11507784 106.12778574 + layer.29.1 0.11363094 73.07844337 + layer.39.0 38.15331751 2499.20168736 + layer.39.1 50.78157832 2507.60538045 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 654.25732064 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4066976 +BPFP 0.3161 bits/point +EBPFP 0.3161 equivalent bits/point +MSE 654.257321 +---------------------- --------------------------------------------------------- +Time: 22.315s Load: 1.307s, Pack+Encode: 7.882s, Decode+Unpack: 13.126s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 654.2573 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001439-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.316s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 319,644B, BPFP=0.1988 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 324,436B, BPFP=0.2017 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 727,796B, BPFP=0.4526 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 737,164B, BPFP=0.4584 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 744,876B, BPFP=0.4632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 761,104B, BPFP=0.4733 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 454,724B, BPFP=0.2828 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 457,200B, BPFP=0.2843 +⌛️ [2/4] FRONTEND: Frontend time: 7.899s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.991s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 4.53468429 + layer.9.1 0.14417255 4.50816507 + layer.19.0 0.04986641 21.71119369 + layer.19.1 0.03935205 7.80020110 + layer.29.0 4.19438972 50.60060490 + layer.29.1 0.10069272 56.52050501 + layer.39.0 8.54645341 2437.32776186 + layer.39.1 8.58293537 2599.76265521 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 647.84572139 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4526944 +BPFP 0.3519 bits/point +EBPFP 0.3519 equivalent bits/point +MSE 647.845721 +---------------------- --------------------------------------------------------- +Time: 22.206s Load: 1.316s, Pack+Encode: 7.899s, Decode+Unpack: 12.991s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 647.8457 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001452-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.318s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,244B, BPFP=0.2029 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 323,148B, BPFP=0.2009 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 742,808B, BPFP=0.4619 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 745,188B, BPFP=0.4634 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 845,120B, BPFP=0.5255 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 879,888B, BPFP=0.5471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 542,052B, BPFP=0.3371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 574,176B, BPFP=0.3570 +⌛️ [2/4] FRONTEND: Frontend time: 7.942s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.165s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 4.53113869 + layer.9.1 0.14191958 4.52319064 + layer.19.0 0.11064845 12.26465372 + layer.19.1 0.11258393 21.05509044 + layer.29.0 0.14067722 161.11897087 + layer.29.1 0.15898021 221.43954951 + layer.39.0 18.90648132 2568.61588666 + layer.39.1 12.01175482 2753.67112385 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 718.40245055 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4978624 +BPFP 0.3870 bits/point +EBPFP 0.3870 equivalent bits/point +MSE 718.402451 +---------------------- --------------------------------------------------------- +Time: 22.425s Load: 1.318s, Pack+Encode: 7.942s, Decode+Unpack: 13.165s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 718.4025 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001464-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.323s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 321,096B, BPFP=0.1997 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 313,752B, BPFP=0.1951 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 769,684B, BPFP=0.4786 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 759,780B, BPFP=0.4724 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 870,364B, BPFP=0.5412 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 862,588B, BPFP=0.5364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,400B, BPFP=0.3528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 567,840B, BPFP=0.3531 +⌛️ [2/4] FRONTEND: Frontend time: 7.774s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.090s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 4.59822210 + layer.9.1 0.03265336 4.59773273 + layer.19.0 0.11338584 7.22617760 + layer.19.1 0.11737041 16.19894018 + layer.29.0 0.14518043 180.57599093 + layer.29.1 0.15176190 206.01042662 + layer.39.0 10.84722720 2695.20312003 + layer.39.1 10.76635501 2702.92359121 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 727.16677517 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5032504 +BPFP 0.3912 bits/point +EBPFP 0.3912 equivalent bits/point +MSE 727.166775 +---------------------- --------------------------------------------------------- +Time: 22.187s Load: 1.323s, Pack+Encode: 7.774s, Decode+Unpack: 13.090s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 727.1668 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001478-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.281s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 322,968B, BPFP=0.2008 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 327,952B, BPFP=0.2039 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 711,932B, BPFP=0.4427 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 722,976B, BPFP=0.4496 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 891,412B, BPFP=0.5543 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 844,088B, BPFP=0.5249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 551,260B, BPFP=0.3428 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 560,720B, BPFP=0.3487 +⌛️ [2/4] FRONTEND: Frontend time: 7.936s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.131s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 4.56203923 + layer.9.1 0.14310633 4.54968374 + layer.19.0 0.11868409 53.07756785 + layer.19.1 0.12162521 39.78900281 + layer.29.0 0.16395149 277.48660856 + layer.29.1 0.12259847 143.97437122 + layer.39.0 330.19024594 3453.81948424 + layer.39.1 213.90321554 3389.11747851 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 920.79702952 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4933308 +BPFP 0.3835 bits/point +EBPFP 0.3835 equivalent bits/point +MSE 920.797030 +---------------------- --------------------------------------------------------- +Time: 22.348s Load: 1.281s, Pack+Encode: 7.936s, Decode+Unpack: 13.131s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 920.7970 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001495-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,712B, BPFP=0.1963 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 324,812B, BPFP=0.2020 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 632,812B, BPFP=0.3935 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 672,600B, BPFP=0.4182 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 752,944B, BPFP=0.4682 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 799,688B, BPFP=0.4973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,204B, BPFP=0.2955 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 482,792B, BPFP=0.3002 +⌛️ [2/4] FRONTEND: Frontend time: 7.925s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.139s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 4.47245136 + layer.9.1 0.14187113 4.45678781 + layer.19.0 0.03719415 7.78937216 + layer.19.1 0.03715970 7.79426275 + layer.29.0 0.14992467 137.85944962 + layer.29.1 0.21581549 204.65588188 + layer.39.0 54.12547258 2545.96895893 + layer.39.1 37.28096148 2523.40178287 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 679.54986842 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4456564 +BPFP 0.3464 bits/point +EBPFP 0.3464 equivalent bits/point +MSE 679.549868 +---------------------- --------------------------------------------------------- +Time: 22.369s Load: 1.305s, Pack+Encode: 7.925s, Decode+Unpack: 13.139s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 679.5499 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 333,688B, BPFP=0.2075 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 343,180B, BPFP=0.2134 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 845,900B, BPFP=0.5260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 843,488B, BPFP=0.5245 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 944,396B, BPFP=0.5872 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 940,496B, BPFP=0.5848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,888B, BPFP=0.3569 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 577,832B, BPFP=0.3593 +⌛️ [2/4] FRONTEND: Frontend time: 8.024s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.224s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 4.46936964 + layer.9.1 0.14222666 4.60639059 + layer.19.0 0.12883153 21.62369667 + layer.19.1 0.12450899 31.11414806 + layer.29.0 0.12456659 170.03122015 + layer.29.1 0.12180437 209.09055635 + layer.39.0 16.93397679 3023.16714422 + layer.39.1 11.63264585 2868.92040751 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 791.62786665 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5402868 +BPFP 0.4199 bits/point +EBPFP 0.4199 equivalent bits/point +MSE 791.627867 +---------------------- --------------------------------------------------------- +Time: 22.556s Load: 1.308s, Pack+Encode: 8.024s, Decode+Unpack: 13.224s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 791.6279 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001520-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,444B, BPFP=0.1899 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 308,480B, BPFP=0.1918 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 579,264B, BPFP=0.3602 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 567,228B, BPFP=0.3527 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 693,776B, BPFP=0.4314 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 689,236B, BPFP=0.4286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 439,252B, BPFP=0.2731 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 461,856B, BPFP=0.2872 +⌛️ [2/4] FRONTEND: Frontend time: 7.854s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.140s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 4.65293168 + layer.9.1 0.14320703 4.63132138 + layer.19.0 0.18609190 9.38462945 + layer.19.1 0.20413370 73.29785001 + layer.29.0 0.16595908 69.70559237 + layer.29.1 0.17797341 146.14128661 + layer.39.0 9.44991518 2659.42406877 + layer.39.1 9.33992148 2722.24896530 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 711.18583070 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4044536 +BPFP 0.3144 bits/point +EBPFP 0.3144 equivalent bits/point +MSE 711.185831 +---------------------- --------------------------------------------------------- +Time: 22.302s Load: 1.308s, Pack+Encode: 7.854s, Decode+Unpack: 13.140s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 711.1858 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001571-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 308,488B, BPFP=0.1918 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 308,692B, BPFP=0.1919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 628,996B, BPFP=0.3911 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 633,556B, BPFP=0.3940 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 683,576B, BPFP=0.4251 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 688,316B, BPFP=0.4280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 453,572B, BPFP=0.2820 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 455,408B, BPFP=0.2832 +⌛️ [2/4] FRONTEND: Frontend time: 7.925s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.917s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 4.59101650 + layer.9.1 0.14264699 4.58496032 + layer.19.0 0.04840791 8.70227373 + layer.19.1 0.04358378 40.82937858 + layer.29.0 4.25626169 118.03564748 + layer.29.1 4.25716892 130.94037528 + layer.39.0 36.32893585 2542.91563196 + layer.39.1 22.75239275 2344.75278574 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 649.41900870 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4160604 +BPFP 0.3234 bits/point +EBPFP 0.3234 equivalent bits/point +MSE 649.419009 +---------------------- --------------------------------------------------------- +Time: 22.156s Load: 1.313s, Pack+Encode: 7.925s, Decode+Unpack: 12.917s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 649.4190 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001605-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 334,788B, BPFP=0.2082 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 345,836B, BPFP=0.2150 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 770,328B, BPFP=0.4790 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 754,768B, BPFP=0.4693 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 890,108B, BPFP=0.5535 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 902,496B, BPFP=0.5612 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,916B, BPFP=0.3320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 559,684B, BPFP=0.3480 +⌛️ [2/4] FRONTEND: Frontend time: 7.864s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.565s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 4.59137622 + layer.9.1 0.14259219 4.61100354 + layer.19.0 0.15398767 72.73733485 + layer.19.1 0.14449470 37.03499333 + layer.29.0 0.17467273 273.65870742 + layer.29.1 0.17545724 234.08968083 + layer.39.0 16.22751761 3014.14040115 + layer.39.1 26.19674268 3019.27666348 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 832.51752010 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5091924 +BPFP 0.3958 bits/point +EBPFP 0.3958 equivalent bits/point +MSE 832.517520 +---------------------- --------------------------------------------------------- +Time: 21.737s Load: 1.309s, Pack+Encode: 7.864s, Decode+Unpack: 12.565s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 832.5175 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001617-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.300s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,404B, BPFP=0.2061 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 337,756B, BPFP=0.2100 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 833,164B, BPFP=0.5181 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 847,544B, BPFP=0.5270 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 968,672B, BPFP=0.6023 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,001,724B, BPFP=0.6229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 576,844B, BPFP=0.3587 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 589,692B, BPFP=0.3667 +⌛️ [2/4] FRONTEND: Frontend time: 8.037s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 4.58656336 + layer.9.1 0.14283950 4.55113600 + layer.19.0 0.09585176 35.36642391 + layer.19.1 0.13229247 57.78690107 + layer.29.0 0.10926771 97.32314550 + layer.29.1 0.10983113 139.50083572 + layer.39.0 13.84559555 3025.95192614 + layer.39.1 12.75833856 3158.18911175 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 815.40700543 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5486800 +BPFP 0.4265 bits/point +EBPFP 0.4265 equivalent bits/point +MSE 815.407005 +---------------------- --------------------------------------------------------- +Time: 22.313s Load: 1.300s, Pack+Encode: 8.037s, Decode+Unpack: 12.976s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 815.4070 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001630-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.287s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,840B, BPFP=0.2213 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 335,864B, BPFP=0.2088 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 849,468B, BPFP=0.5282 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 792,368B, BPFP=0.4927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 927,284B, BPFP=0.5766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 930,172B, BPFP=0.5784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 575,252B, BPFP=0.3577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 566,468B, BPFP=0.3522 +⌛️ [2/4] FRONTEND: Frontend time: 8.032s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.213s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 4.51219133 + layer.9.1 0.14345678 4.57226780 + layer.19.0 0.16166856 108.29863101 + layer.19.1 0.14880180 95.38727714 + layer.29.0 0.17070711 192.80074021 + layer.29.1 0.15868870 270.43047596 + layer.39.0 31.98565594 3005.80770455 + layer.39.1 38.57007372 2925.06367399 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 825.85912025 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5332716 +BPFP 0.4145 bits/point +EBPFP 0.4145 equivalent bits/point +MSE 825.859120 +---------------------- --------------------------------------------------------- +Time: 22.532s Load: 1.287s, Pack+Encode: 8.032s, Decode+Unpack: 13.213s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 825.8591 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001636-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 310,604B, BPFP=0.1931 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 315,564B, BPFP=0.1962 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 644,180B, BPFP=0.4006 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 674,224B, BPFP=0.4192 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 750,032B, BPFP=0.4664 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 782,980B, BPFP=0.4869 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,240B, BPFP=0.3297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 539,644B, BPFP=0.3356 +⌛️ [2/4] FRONTEND: Frontend time: 7.913s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.175s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 4.56150509 + layer.9.1 0.03218400 4.59124905 + layer.19.0 0.03742503 25.88062868 + layer.19.1 0.04139693 40.08005263 + layer.29.0 0.11425402 106.45998488 + layer.29.1 0.11776626 128.18402778 + layer.39.0 23.31748448 3015.83508437 + layer.39.1 15.89369429 2830.82712512 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 769.55245720 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4547468 +BPFP 0.3535 bits/point +EBPFP 0.3535 equivalent bits/point +MSE 769.552457 +---------------------- --------------------------------------------------------- +Time: 22.364s Load: 1.276s, Pack+Encode: 7.913s, Decode+Unpack: 13.175s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 769.5525 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001639-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.319s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 320,760B, BPFP=0.1995 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 322,076B, BPFP=0.2003 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 713,008B, BPFP=0.4434 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 728,952B, BPFP=0.4533 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 768,840B, BPFP=0.4781 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 775,116B, BPFP=0.4820 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 442,928B, BPFP=0.2754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 447,720B, BPFP=0.2784 +⌛️ [2/4] FRONTEND: Frontend time: 7.917s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.197s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 4.51319805 + layer.9.1 0.14315520 4.51373748 + layer.19.0 0.04114968 18.30192664 + layer.19.1 0.04120060 13.44839173 + layer.29.0 0.18627036 191.74223973 + layer.29.1 0.17990809 174.05625199 + layer.39.0 46.02158449 2468.14899713 + layer.39.1 44.38447151 2414.66157275 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 661.17328944 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4519400 +BPFP 0.3513 bits/point +EBPFP 0.3513 equivalent bits/point +MSE 661.173289 +---------------------- --------------------------------------------------------- +Time: 22.433s Load: 1.319s, Pack+Encode: 7.917s, Decode+Unpack: 13.197s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 661.1733 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001653-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.307s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 316,768B, BPFP=0.1970 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 315,184B, BPFP=0.1960 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 721,336B, BPFP=0.4485 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 774,228B, BPFP=0.4814 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 848,608B, BPFP=0.5277 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 846,072B, BPFP=0.5261 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 542,260B, BPFP=0.3372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 546,568B, BPFP=0.3399 +⌛️ [2/4] FRONTEND: Frontend time: 7.969s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.136s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64482133 4.61527293 + layer.9.1 0.03141260 4.57531252 + layer.19.0 3.18767318 7.61362760 + layer.19.1 3.18914595 12.04133710 + layer.29.0 4.14946039 57.51473456 + layer.29.1 4.13952905 63.15250915 + layer.39.0 7.50609877 2416.09837631 + layer.39.1 7.79272438 2424.29528812 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 623.73830729 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4911024 +BPFP 0.3817 bits/point +EBPFP 0.3817 equivalent bits/point +MSE 623.738307 +---------------------- --------------------------------------------------------- +Time: 22.412s Load: 1.307s, Pack+Encode: 7.969s, Decode+Unpack: 13.136s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 623.7383 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,920B, BPFP=0.2033 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 318,452B, BPFP=0.1980 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 754,476B, BPFP=0.4691 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 719,068B, BPFP=0.4471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 923,684B, BPFP=0.5744 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 922,132B, BPFP=0.5734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 506,016B, BPFP=0.3146 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 512,488B, BPFP=0.3187 +⌛️ [2/4] FRONTEND: Frontend time: 7.975s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 4.53198095 + layer.9.1 0.14140505 4.83633950 + layer.19.0 0.11753838 7.82271644 + layer.19.1 0.11213660 35.15312848 + layer.29.0 0.21817993 286.00224849 + layer.29.1 4.26279853 170.63164597 + layer.39.0 8.71778059 2498.23288762 + layer.39.1 8.43609532 2321.15584209 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 666.04584869 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4983236 +BPFP 0.3873 bits/point +EBPFP 0.3873 equivalent bits/point +MSE 666.045849 +---------------------- --------------------------------------------------------- +Time: 22.310s Load: 1.309s, Pack+Encode: 7.975s, Decode+Unpack: 13.025s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 666.0458 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001659-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 329,384B, BPFP=0.2048 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 342,160B, BPFP=0.2128 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 687,740B, BPFP=0.4276 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 762,284B, BPFP=0.4740 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 785,896B, BPFP=0.4887 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 845,872B, BPFP=0.5260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,096B, BPFP=0.2948 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 507,876B, BPFP=0.3158 +⌛️ [2/4] FRONTEND: Frontend time: 7.893s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.142s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 4.78861137 + layer.9.1 0.11967093 4.63935401 + layer.19.0 0.14332279 10.07880775 + layer.19.1 0.14205440 28.51128472 + layer.29.0 0.15356100 73.34153136 + layer.29.1 0.14462723 101.79914836 + layer.39.0 8.04224558 2699.05125756 + layer.39.1 10.17930073 2864.52594715 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 723.34199279 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4735308 +BPFP 0.3681 bits/point +EBPFP 0.3681 equivalent bits/point +MSE 723.341993 +---------------------- --------------------------------------------------------- +Time: 22.347s Load: 1.312s, Pack+Encode: 7.893s, Decode+Unpack: 13.142s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 723.3420 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001671-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 316,608B, BPFP=0.1969 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 314,420B, BPFP=0.1955 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 625,104B, BPFP=0.3887 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 615,220B, BPFP=0.3826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 688,496B, BPFP=0.4281 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 683,792B, BPFP=0.4252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 490,412B, BPFP=0.3049 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 499,500B, BPFP=0.3106 +⌛️ [2/4] FRONTEND: Frontend time: 7.962s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.139s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 4.56794245 + layer.9.1 0.00091860 4.57772331 + layer.19.0 3.15620088 8.02080908 + layer.19.1 3.15238324 16.91079423 + layer.29.0 4.13387767 56.05763491 + layer.29.1 4.13737010 49.17464084 + layer.39.0 41.03603550 2027.13642152 + layer.39.1 41.15380502 2031.86803566 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 524.78925025 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4233552 +BPFP 0.3291 bits/point +EBPFP 0.3291 equivalent bits/point +MSE 524.789250 +---------------------- --------------------------------------------------------- +Time: 22.408s Load: 1.308s, Pack+Encode: 7.962s, Decode+Unpack: 13.139s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 524.7893 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001694-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 329,908B, BPFP=0.2051 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 339,836B, BPFP=0.2113 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 679,008B, BPFP=0.4222 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 713,604B, BPFP=0.4437 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 808,872B, BPFP=0.5030 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 867,488B, BPFP=0.5394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 531,176B, BPFP=0.3303 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 541,052B, BPFP=0.3364 +⌛️ [2/4] FRONTEND: Frontend time: 7.900s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.165s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 4.46684102 + layer.9.1 0.14279730 4.47279771 + layer.19.0 0.12708100 39.67800860 + layer.19.1 0.11978473 43.69507621 + layer.29.0 0.14591184 129.30559535 + layer.29.1 0.16402206 164.55368513 + layer.39.0 105.60261461 2609.40894620 + layer.39.1 191.64541547 3028.87806431 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 753.05737682 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4810944 +BPFP 0.3739 bits/point +EBPFP 0.3739 equivalent bits/point +MSE 753.057377 +---------------------- --------------------------------------------------------- +Time: 22.375s Load: 1.310s, Pack+Encode: 7.900s, Decode+Unpack: 13.165s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 753.0574 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001712-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.318s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,372B, BPFP=0.1886 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 304,344B, BPFP=0.1892 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 713,612B, BPFP=0.4437 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 727,064B, BPFP=0.4521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 848,376B, BPFP=0.5275 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 816,596B, BPFP=0.5078 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 580,024B, BPFP=0.3607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 572,656B, BPFP=0.3561 +⌛️ [2/4] FRONTEND: Frontend time: 7.915s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.925s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 4.53086385 + layer.9.1 0.14187527 4.56312275 + layer.19.0 0.05966252 21.62021948 + layer.19.1 0.05602499 30.79260188 + layer.29.0 0.10851584 79.89138610 + layer.29.1 0.10663395 89.85969834 + layer.39.0 36.66006795 2872.09551098 + layer.39.1 37.39855191 2891.12957657 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 749.31037249 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4866044 +BPFP 0.3782 bits/point +EBPFP 0.3782 equivalent bits/point +MSE 749.310372 +---------------------- --------------------------------------------------------- +Time: 22.159s Load: 1.318s, Pack+Encode: 7.915s, Decode+Unpack: 12.925s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 749.3104 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001750-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.324s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,140B, BPFP=0.1885 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 305,440B, BPFP=0.1899 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 739,972B, BPFP=0.4601 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 744,500B, BPFP=0.4629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 769,480B, BPFP=0.4785 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 789,020B, BPFP=0.4906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,436B, BPFP=0.2876 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 471,244B, BPFP=0.2930 +⌛️ [2/4] FRONTEND: Frontend time: 7.937s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.042s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 4.59540061 + layer.9.1 0.11247108 4.57838119 + layer.19.0 0.01001183 13.57643366 + layer.19.1 3.17262087 9.90306692 + layer.29.0 0.16690336 102.34790871 + layer.29.1 0.17317613 88.93991762 + layer.39.0 33.55914965 2674.13180516 + layer.39.1 10.63762287 2421.99140401 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 665.00803973 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4585232 +BPFP 0.3564 bits/point +EBPFP 0.3564 equivalent bits/point +MSE 665.008040 +---------------------- --------------------------------------------------------- +Time: 22.303s Load: 1.324s, Pack+Encode: 7.937s, Decode+Unpack: 13.042s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 665.0080 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,356B, BPFP=0.1924 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 318,356B, BPFP=0.1980 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 680,060B, BPFP=0.4229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 705,024B, BPFP=0.4384 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 850,076B, BPFP=0.5286 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 929,972B, BPFP=0.5783 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 488,372B, BPFP=0.3037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 517,792B, BPFP=0.3220 +⌛️ [2/4] FRONTEND: Frontend time: 7.968s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 4.52046055 + layer.9.1 0.03247940 4.57149675 + layer.19.0 0.20408508 122.59672477 + layer.19.1 0.20919449 67.27508158 + layer.29.0 0.13400092 155.44234519 + layer.29.1 0.12260655 251.28625438 + layer.39.0 13.98719058 2524.72811207 + layer.39.1 8.64389327 2622.74243871 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 719.14536425 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4799008 +BPFP 0.3730 bits/point +EBPFP 0.3730 equivalent bits/point +MSE 719.145364 +---------------------- --------------------------------------------------------- +Time: 22.357s Load: 1.317s, Pack+Encode: 7.968s, Decode+Unpack: 13.072s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 719.1454 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001838-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 314,940B, BPFP=0.1958 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 318,900B, BPFP=0.1983 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 715,392B, BPFP=0.4448 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 717,564B, BPFP=0.4462 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 804,832B, BPFP=0.5005 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 814,980B, BPFP=0.5068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 513,348B, BPFP=0.3192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 506,168B, BPFP=0.3147 +⌛️ [2/4] FRONTEND: Frontend time: 7.916s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.042s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 4.51996217 + layer.9.1 0.14463072 4.50828415 + layer.19.0 0.16931463 79.94129059 + layer.19.1 0.17979540 84.10954911 + layer.29.0 0.11737749 111.11175780 + layer.29.1 0.10948915 77.43758954 + layer.39.0 8.46774266 2516.28000637 + layer.39.1 8.48397517 2617.11111111 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 686.87744385 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4706124 +BPFP 0.3658 bits/point +EBPFP 0.3658 equivalent bits/point +MSE 686.877444 +---------------------- --------------------------------------------------------- +Time: 22.275s Load: 1.317s, Pack+Encode: 7.916s, Decode+Unpack: 13.042s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 686.8774 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001840-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 327,988B, BPFP=0.2039 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 329,924B, BPFP=0.2052 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 765,260B, BPFP=0.4759 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 733,732B, BPFP=0.4562 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 959,112B, BPFP=0.5964 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 954,084B, BPFP=0.5933 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,076B, BPFP=0.3402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 533,704B, BPFP=0.3319 +⌛️ [2/4] FRONTEND: Frontend time: 7.915s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.143s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14223057 4.47062353 + layer.9.1 0.14268742 4.45159720 + layer.19.0 0.21739516 58.46584487 + layer.19.1 0.24972380 57.64514187 + layer.29.0 0.18828982 268.91897485 + layer.29.1 0.18108670 306.49176218 + layer.39.0 11.67542184 2937.07004139 + layer.39.1 15.11985385 2830.93027698 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 808.55553286 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5150880 +BPFP 0.4004 bits/point +EBPFP 0.4004 equivalent bits/point +MSE 808.555533 +---------------------- --------------------------------------------------------- +Time: 22.370s Load: 1.312s, Pack+Encode: 7.915s, Decode+Unpack: 13.143s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 808.5555 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001854-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,392B, BPFP=0.2061 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 327,820B, BPFP=0.2038 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 758,180B, BPFP=0.4714 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 769,700B, BPFP=0.4786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 879,248B, BPFP=0.5467 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 874,796B, BPFP=0.5440 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,076B, BPFP=0.3203 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 518,400B, BPFP=0.3223 +⌛️ [2/4] FRONTEND: Frontend time: 7.875s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.947s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 4.51725509 + layer.9.1 0.14270393 4.54200276 + layer.19.0 0.11367196 48.79296502 + layer.19.1 0.12267420 38.96592447 + layer.29.0 0.13560262 134.25747174 + layer.29.1 0.14809222 173.58104505 + layer.39.0 10.32325245 2724.22667940 + layer.39.1 8.35688960 2572.75803884 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 712.70517280 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4974612 +BPFP 0.3867 bits/point +EBPFP 0.3867 equivalent bits/point +MSE 712.705173 +---------------------- --------------------------------------------------------- +Time: 22.087s Load: 1.265s, Pack+Encode: 7.875s, Decode+Unpack: 12.947s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 712.7052 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001855-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 296,708B, BPFP=0.1845 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 302,756B, BPFP=0.1883 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 612,064B, BPFP=0.3806 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 645,504B, BPFP=0.4014 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 719,300B, BPFP=0.4473 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 756,544B, BPFP=0.4704 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,516B, BPFP=0.2857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 474,008B, BPFP=0.2947 +⌛️ [2/4] FRONTEND: Frontend time: 7.965s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.118s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61171023 4.68530499 + layer.9.1 2.72679972 4.64589738 + layer.19.0 0.11263356 26.64103042 + layer.19.1 0.10212393 17.43522540 + layer.29.0 4.19513435 71.52498209 + layer.29.1 4.21594343 139.89004298 + layer.39.0 8.80532175 2762.53040433 + layer.39.1 9.27097449 2824.29544731 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 731.45604186 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4266400 +BPFP 0.3316 bits/point +EBPFP 0.3316 equivalent bits/point +MSE 731.456042 +---------------------- --------------------------------------------------------- +Time: 22.389s Load: 1.306s, Pack+Encode: 7.965s, Decode+Unpack: 13.118s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 731.4560 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001857-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.318s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 340,772B, BPFP=0.2119 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 341,008B, BPFP=0.2120 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 704,932B, BPFP=0.4383 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 731,476B, BPFP=0.4548 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 888,072B, BPFP=0.5522 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 903,768B, BPFP=0.5620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 491,840B, BPFP=0.3058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 505,568B, BPFP=0.3144 +⌛️ [2/4] FRONTEND: Frontend time: 7.931s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.956s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 4.54348859 + layer.9.1 0.14997165 4.54456215 + layer.19.0 0.15685862 67.48734479 + layer.19.1 0.13652294 62.54224371 + layer.29.0 0.22636045 259.76201847 + layer.29.1 0.21023706 227.43893266 + layer.39.0 31.35143565 2688.33460681 + layer.39.1 33.65704095 2567.26297358 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 735.23952135 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4907436 +BPFP 0.3814 bits/point +EBPFP 0.3814 equivalent bits/point +MSE 735.239521 +---------------------- --------------------------------------------------------- +Time: 22.205s Load: 1.318s, Pack+Encode: 7.931s, Decode+Unpack: 12.956s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 735.2395 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 336,992B, BPFP=0.2095 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 335,228B, BPFP=0.2085 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 750,036B, BPFP=0.4664 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 801,232B, BPFP=0.4982 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 892,452B, BPFP=0.5549 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 881,968B, BPFP=0.5484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 561,040B, BPFP=0.3489 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 552,012B, BPFP=0.3433 +⌛️ [2/4] FRONTEND: Frontend time: 7.915s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.090s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 4.48667572 + layer.9.1 0.14194651 4.47671174 + layer.19.0 0.13165920 44.11647365 + layer.19.1 0.11547583 30.65635695 + layer.29.0 4.19202371 155.00929242 + layer.29.1 0.11136677 87.59570996 + layer.39.0 9.51575185 2837.38936644 + layer.39.1 9.66679849 2721.34957020 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 735.63501964 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5110960 +BPFP 0.3973 bits/point +EBPFP 0.3973 equivalent bits/point +MSE 735.635020 +---------------------- --------------------------------------------------------- +Time: 22.318s Load: 1.313s, Pack+Encode: 7.915s, Decode+Unpack: 13.090s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 735.6350 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 284,348B, BPFP=0.1768 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 286,308B, BPFP=0.1780 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 532,108B, BPFP=0.3309 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 531,056B, BPFP=0.3302 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 569,356B, BPFP=0.3540 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 568,628B, BPFP=0.3536 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 430,512B, BPFP=0.2677 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 431,164B, BPFP=0.2681 +⌛️ [2/4] FRONTEND: Frontend time: 7.969s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.105s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.60361947 4.56376322 + layer.9.1 2.64162177 4.54196265 + layer.19.0 3.15421573 32.45648778 + layer.19.1 3.18597002 18.71452414 + layer.29.0 4.16148507 46.45170726 + layer.29.1 4.16879732 48.50640720 + layer.39.0 7.32495125 2249.29083095 + layer.39.1 7.16856507 2301.25246737 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 588.22226882 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3633480 +BPFP 0.2824 bits/point +EBPFP 0.2824 equivalent bits/point +MSE 588.222269 +---------------------- --------------------------------------------------------- +Time: 22.385s Load: 1.310s, Pack+Encode: 7.969s, Decode+Unpack: 13.105s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 588.2223 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.3708 bits/point +Avg EBPFP 0.3708 equivalent bits/point +Avg MSE 716.642758 +Avg Time 22.292s +------------------------ ---------------------------- diff --git a/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..42ad4f49a86aa483ea0f757f93fa8b1a0b91ed1a --- /dev/null +++ b/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_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/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-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 hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.295s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 538,244B, BPFP=0.3347 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 557,372B, BPFP=0.3466 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,240,128B, BPFP=0.7711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,275,232B, BPFP=0.7930 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,269,700B, BPFP=0.7895 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,328,952B, BPFP=0.8264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 844,692B, BPFP=0.5252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 863,672B, BPFP=0.5370 +⌛️ [2/4] FRONTEND: Frontend time: 8.122s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.311s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11100285 4.52125866 + layer.9.1 0.11103876 12.45570554 + layer.19.0 0.02553116 40.11734420 + layer.19.1 0.10833414 104.15989136 + layer.29.0 0.30844607 285.21527778 + layer.29.1 0.33610574 423.29552690 + layer.39.0 10.03071710 1428.77809615 + layer.39.1 10.11984639 1553.50939191 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 481.50656156 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7917992 +BPFP 0.6154 bits/point +EBPFP 0.6154 equivalent bits/point +MSE 481.506562 +---------------------- --------------------------------------------------------- +Time: 22.729s Load: 1.295s, Pack+Encode: 8.122s, Decode+Unpack: 13.311s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 481.5066 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000001-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.294s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 500,312B, BPFP=0.3111 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 507,660B, BPFP=0.3157 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,231,896B, BPFP=0.7660 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,278,800B, BPFP=0.7952 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,289,480B, BPFP=0.8018 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,355,180B, BPFP=0.8427 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 904,024B, BPFP=0.5621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 936,908B, BPFP=0.5826 +⌛️ [2/4] FRONTEND: Frontend time: 7.654s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.841s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61021196 4.38942272 + layer.9.1 2.61901253 4.38655615 + layer.19.0 3.15140481 15.36490543 + layer.19.1 3.16250889 15.57907514 + layer.29.0 4.15625404 137.79663125 + layer.29.1 4.15938147 46.53117538 + layer.39.0 10.95910936 1385.47150589 + layer.39.1 9.06533984 1351.04568609 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 370.07061975 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8004260 +BPFP 0.6221 bits/point +EBPFP 0.6221 equivalent bits/point +MSE 370.070620 +---------------------- --------------------------------------------------------- +Time: 21.789s Load: 1.294s, Pack+Encode: 7.654s, Decode+Unpack: 12.841s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 370.0706 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000045-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.296s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 611,508B, BPFP=0.3802 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 621,660B, BPFP=0.3866 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,373,272B, BPFP=0.8539 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,388,128B, BPFP=0.8632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,605,812B, BPFP=0.9985 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,659,048B, BPFP=1.0316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,071,584B, BPFP=0.6663 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,073,036B, BPFP=0.6672 +⌛️ [2/4] FRONTEND: Frontend time: 7.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.633s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 8.75906297 + layer.9.1 0.14253284 28.19401663 + layer.19.0 0.09744245 87.73557386 + layer.19.1 0.13747554 149.05282951 + layer.29.0 4.19766265 94.99490608 + layer.29.1 4.20130152 65.46777997 + layer.39.0 38.53896798 1722.26408787 + layer.39.1 35.26563495 1751.51066539 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 488.49736529 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9404048 +BPFP 0.7309 bits/point +EBPFP 0.7309 equivalent bits/point +MSE 488.497365 +---------------------- --------------------------------------------------------- +Time: 21.544s Load: 1.296s, Pack+Encode: 7.615s, Decode+Unpack: 12.633s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 488.4974 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000064-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 549,436B, BPFP=0.3416 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 520,544B, BPFP=0.3237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,313,120B, BPFP=0.8165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,314,628B, BPFP=0.8175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,584,700B, BPFP=0.9854 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,533,140B, BPFP=0.9533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,022,268B, BPFP=0.6357 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,005,964B, BPFP=0.6255 +⌛️ [2/4] FRONTEND: Frontend time: 7.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.788s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 12.27548825 + layer.9.1 0.03225276 4.30636703 + layer.19.0 0.11899935 74.44320081 + layer.19.1 0.11456829 29.30408061 + layer.29.0 0.13249551 129.48816062 + layer.29.1 0.12471250 99.97785339 + layer.39.0 10.78219516 1795.62909901 + layer.39.1 9.99374328 1699.34781917 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 480.59650861 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8843800 +BPFP 0.6874 bits/point +EBPFP 0.6874 equivalent bits/point +MSE 480.596509 +---------------------- --------------------------------------------------------- +Time: 21.314s Load: 1.226s, Pack+Encode: 7.299s, Decode+Unpack: 12.788s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 480.5965 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000092-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 490,836B, BPFP=0.3052 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 490,792B, BPFP=0.3052 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,293,448B, BPFP=0.8043 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,224,404B, BPFP=0.7614 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,349,084B, BPFP=0.8389 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,318,340B, BPFP=0.8198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 893,840B, BPFP=0.5558 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 890,568B, BPFP=0.5538 +⌛️ [2/4] FRONTEND: Frontend time: 7.786s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.754s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 4.35113562 + layer.9.1 0.03227402 4.35409609 + layer.19.0 3.18865969 10.61206498 + layer.19.1 3.19251184 10.82841850 + layer.29.0 0.19572780 269.78995543 + layer.29.1 0.14992644 105.00431789 + layer.39.0 12.23891426 1539.67176059 + layer.39.1 9.64680585 1541.21267112 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 435.72805253 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7951312 +BPFP 0.6180 bits/point +EBPFP 0.6180 equivalent bits/point +MSE 435.728053 +---------------------- --------------------------------------------------------- +Time: 21.792s Load: 1.252s, Pack+Encode: 7.786s, Decode+Unpack: 12.754s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 435.7281 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000096-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 518,872B, BPFP=0.3226 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 529,420B, BPFP=0.3292 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,353,612B, BPFP=0.8417 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,319,280B, BPFP=0.8203 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,415,376B, BPFP=0.8801 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,447,140B, BPFP=0.8999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 965,424B, BPFP=0.6003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 950,348B, BPFP=0.5909 +⌛️ [2/4] FRONTEND: Frontend time: 7.558s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.707s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 4.34955248 + layer.9.1 0.14248663 4.33811759 + layer.19.0 0.04071400 6.32181792 + layer.19.1 0.03715074 10.83720974 + layer.29.0 4.22673132 81.86239951 + layer.29.1 4.22861263 111.01082458 + layer.39.0 10.70292353 1502.08564152 + layer.39.1 9.44238934 1451.12862146 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 396.49177310 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8499472 +BPFP 0.6606 bits/point +EBPFP 0.6606 equivalent bits/point +MSE 396.491773 +---------------------- --------------------------------------------------------- +Time: 21.570s Load: 1.305s, Pack+Encode: 7.558s, Decode+Unpack: 12.707s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 396.4918 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000133-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.263s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,392B, BPFP=0.3926 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 623,936B, BPFP=0.3880 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,392,088B, BPFP=0.8656 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,388,116B, BPFP=0.8632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,592,420B, BPFP=0.9902 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,596,572B, BPFP=0.9928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,057,632B, BPFP=0.6577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 954,804B, BPFP=0.5937 +⌛️ [2/4] FRONTEND: Frontend time: 7.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.767s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 49.13314828 + layer.9.1 0.14203072 53.31799686 + layer.19.0 0.04969746 102.40786175 + layer.19.1 0.04852902 98.56517630 + layer.29.0 0.13952979 168.33335323 + layer.29.1 0.11857529 110.11718004 + layer.39.0 52.16041866 1770.87376632 + layer.39.1 64.85207736 1587.97373448 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 492.59027716 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9236960 +BPFP 0.7180 bits/point +EBPFP 0.7180 equivalent bits/point +MSE 492.590277 +---------------------- --------------------------------------------------------- +Time: 21.634s Load: 1.263s, Pack+Encode: 7.604s, Decode+Unpack: 12.767s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 492.5903 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000196-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 548,360B, BPFP=0.3410 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 545,392B, BPFP=0.3391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,330,288B, BPFP=0.8272 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,308,796B, BPFP=0.8138 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,487,300B, BPFP=0.9248 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,509,124B, BPFP=0.9384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,022,236B, BPFP=0.6356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,010,816B, BPFP=0.6285 +⌛️ [2/4] FRONTEND: Frontend time: 7.776s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.115s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 12.60509815 + layer.9.1 0.14255715 8.46811139 + layer.19.0 0.12077588 39.47155812 + layer.19.1 0.12364273 52.17174069 + layer.29.0 4.20710867 129.77032593 + layer.29.1 4.21108798 112.05594357 + layer.39.0 8.84959445 1545.37026425 + layer.39.1 9.12830806 1409.87567654 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 413.72358983 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8762312 +BPFP 0.6811 bits/point +EBPFP 0.6811 equivalent bits/point +MSE 413.723590 +---------------------- --------------------------------------------------------- +Time: 22.208s Load: 1.317s, Pack+Encode: 7.776s, Decode+Unpack: 13.115s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 413.7236 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000268-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.274s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 622,992B, BPFP=0.3874 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 639,592B, BPFP=0.3977 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,359,128B, BPFP=0.8451 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,362,900B, BPFP=0.8475 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,610,328B, BPFP=1.0013 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,617,568B, BPFP=1.0058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,148,068B, BPFP=0.7139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,232,412B, BPFP=0.7663 +⌛️ [2/4] FRONTEND: Frontend time: 7.867s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 24.65082030 + layer.9.1 0.14262173 28.85736280 + layer.19.0 0.13202983 75.26159563 + layer.19.1 0.12978742 97.83498488 + layer.29.0 0.12169007 65.71364613 + layer.29.1 0.13371499 202.18765918 + layer.39.0 71.22791309 1979.15536453 + layer.39.1 35.82807525 2029.80340656 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 562.93310500 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9592988 +BPFP 0.7456 bits/point +EBPFP 0.7456 equivalent bits/point +MSE 562.933105 +---------------------- --------------------------------------------------------- +Time: 22.180s Load: 1.274s, Pack+Encode: 7.867s, Decode+Unpack: 13.039s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 562.9331 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000315-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 541,936B, BPFP=0.3370 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 534,084B, BPFP=0.3321 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,283,452B, BPFP=0.7981 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,279,960B, BPFP=0.7959 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,558,720B, BPFP=0.9692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,513,180B, BPFP=0.9409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,011,612B, BPFP=0.6290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 947,640B, BPFP=0.5893 +⌛️ [2/4] FRONTEND: Frontend time: 7.728s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 4.39537941 + layer.9.1 0.14121198 4.36978141 + layer.19.0 0.08207523 93.34330229 + layer.19.1 0.11558007 87.51867439 + layer.29.0 0.16338114 245.12058262 + layer.29.1 0.15213004 143.86232490 + layer.39.0 27.31461666 2258.71983445 + layer.39.1 28.69002706 2159.40783190 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 624.59221392 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8670584 +BPFP 0.6739 bits/point +EBPFP 0.6739 equivalent bits/point +MSE 624.592214 +---------------------- --------------------------------------------------------- +Time: 22.084s Load: 1.276s, Pack+Encode: 7.728s, Decode+Unpack: 13.080s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 624.5922 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000322-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.281s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 586,692B, BPFP=0.3648 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 582,552B, BPFP=0.3622 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,412,092B, BPFP=0.8781 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,393,740B, BPFP=0.8667 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,655,888B, BPFP=1.0297 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,647,508B, BPFP=1.0244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,151,660B, BPFP=0.7161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,163,292B, BPFP=0.7234 +⌛️ [2/4] FRONTEND: Frontend time: 7.867s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.772s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 4.31727026 + layer.9.1 0.11112548 12.54668472 + layer.19.0 0.11343976 47.83596983 + layer.19.1 0.08227446 47.68652499 + layer.29.0 0.11178890 46.23225585 + layer.29.1 4.21559211 50.46695419 + layer.39.0 9.18455757 1650.16157275 + layer.39.1 8.88372284 1621.41435848 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 435.08269889 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9593424 +BPFP 0.7457 bits/point +EBPFP 0.7457 equivalent bits/point +MSE 435.082699 +---------------------- --------------------------------------------------------- +Time: 21.920s Load: 1.281s, Pack+Encode: 7.867s, Decode+Unpack: 12.772s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 435.0827 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000347-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.296s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 657,084B, BPFP=0.4086 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 625,972B, BPFP=0.3892 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,270,236B, BPFP=0.7899 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,245,012B, BPFP=0.7742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,573,036B, BPFP=0.9781 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,565,236B, BPFP=0.9733 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 893,520B, BPFP=0.5556 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 893,524B, BPFP=0.5556 +⌛️ [2/4] FRONTEND: Frontend time: 7.824s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.185s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 122.30663005 + layer.9.1 0.14561824 109.97735594 + layer.19.0 0.12576092 39.28987086 + layer.19.1 0.12606844 40.90510088 + layer.29.0 0.19770402 48.65292701 + layer.29.1 0.18863435 92.36990608 + layer.39.0 84.70259273 2254.04871060 + layer.39.1 43.66404011 2111.08818848 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 602.32983624 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8723620 +BPFP 0.6781 bits/point +EBPFP 0.6781 equivalent bits/point +MSE 602.329836 +---------------------- --------------------------------------------------------- +Time: 22.305s Load: 1.296s, Pack+Encode: 7.824s, Decode+Unpack: 13.185s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 602.3298 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000352-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.303s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 531,936B, BPFP=0.3308 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 521,460B, BPFP=0.3243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,231,968B, BPFP=0.7661 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,232,020B, BPFP=0.7661 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,138,480B, BPFP=0.7079 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,102,432B, BPFP=0.6855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 821,416B, BPFP=0.5108 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 807,868B, BPFP=0.5023 +⌛️ [2/4] FRONTEND: Frontend time: 7.835s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.330s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 4.44236416 + layer.9.1 0.14295322 28.97392849 + layer.19.0 0.05949541 52.61783170 + layer.19.1 0.07012351 44.04785001 + layer.29.0 4.21949463 45.63742140 + layer.29.1 4.23773965 144.73302690 + layer.39.0 8.48589099 1296.80356574 + layer.39.1 10.46205428 1356.87551735 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 371.76643822 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7387580 +BPFP 0.5742 bits/point +EBPFP 0.5742 equivalent bits/point +MSE 371.766438 +---------------------- --------------------------------------------------------- +Time: 22.468s Load: 1.303s, Pack+Encode: 7.835s, Decode+Unpack: 13.330s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 371.7664 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000360-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.303s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 534,412B, BPFP=0.3323 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 520,392B, BPFP=0.3236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,269,080B, BPFP=0.7891 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,295,012B, BPFP=0.8053 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,054,552B, BPFP=0.6557 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,028,636B, BPFP=0.6396 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 762,820B, BPFP=0.4743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 763,588B, BPFP=0.4748 +⌛️ [2/4] FRONTEND: Frontend time: 7.759s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.650s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 12.69336621 + layer.9.1 0.00177230 4.48617982 + layer.19.0 0.01183476 24.74230191 + layer.19.1 0.01005667 16.27158573 + layer.29.0 4.18449569 37.64504984 + layer.29.1 4.18053255 29.44143734 + layer.39.0 7.97218927 1177.81614136 + layer.39.1 7.92115618 1240.98392232 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 318.00999807 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7228492 +BPFP 0.5618 bits/point +EBPFP 0.5618 equivalent bits/point +MSE 318.009998 +---------------------- --------------------------------------------------------- +Time: 21.712s Load: 1.303s, Pack+Encode: 7.759s, Decode+Unpack: 12.650s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 318.0100 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000389-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.284s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 558,576B, BPFP=0.3473 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 558,104B, BPFP=0.3470 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,359,060B, BPFP=0.8451 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,381,420B, BPFP=0.8590 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,395,824B, BPFP=0.8679 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,402,356B, BPFP=0.8720 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 971,196B, BPFP=0.6039 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 979,488B, BPFP=0.6091 +⌛️ [2/4] FRONTEND: Frontend time: 7.852s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.449s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 4.34489072 + layer.9.1 0.03324844 4.35428015 + layer.19.0 0.13337831 70.43351043 + layer.19.1 0.12266011 29.81353968 + layer.29.0 4.22871927 243.46171601 + layer.29.1 4.21185188 143.64356495 + layer.39.0 10.68945623 1583.06367399 + layer.39.1 11.70080065 1429.71298949 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 438.60352068 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8606024 +BPFP 0.6689 bits/point +EBPFP 0.6689 equivalent bits/point +MSE 438.603521 +---------------------- --------------------------------------------------------- +Time: 22.585s Load: 1.284s, Pack+Encode: 7.852s, Decode+Unpack: 13.449s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 438.6035 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000429-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.294s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 579,916B, BPFP=0.3606 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 578,844B, BPFP=0.3599 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,255,760B, BPFP=0.7809 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,257,248B, BPFP=0.7818 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,417,232B, BPFP=0.8813 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,407,492B, BPFP=0.8752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 936,500B, BPFP=0.5823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 923,060B, BPFP=0.5740 +⌛️ [2/4] FRONTEND: Frontend time: 7.690s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.424s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 20.95847013 + layer.9.1 0.14233285 65.75375080 + layer.19.0 0.14139387 79.70324936 + layer.19.1 0.13524239 98.36104147 + layer.29.0 0.16019033 72.68762934 + layer.29.1 0.14649145 115.14205269 + layer.39.0 12.41561455 1552.74753263 + layer.39.1 10.59172910 1554.76870423 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 445.01530383 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8356052 +BPFP 0.6495 bits/point +EBPFP 0.6495 equivalent bits/point +MSE 445.015304 +---------------------- --------------------------------------------------------- +Time: 22.409s Load: 1.294s, Pack+Encode: 7.690s, Decode+Unpack: 13.424s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 445.0153 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000436-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.295s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 561,564B, BPFP=0.3492 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 567,456B, BPFP=0.3529 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,286,108B, BPFP=0.7997 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,281,148B, BPFP=0.7966 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,461,656B, BPFP=0.9089 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,457,784B, BPFP=0.9065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 857,672B, BPFP=0.5333 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 861,728B, BPFP=0.5358 +⌛️ [2/4] FRONTEND: Frontend time: 7.785s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.098s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 8.27931553 + layer.9.1 0.03247534 8.46728935 + layer.19.0 0.03739121 10.84681804 + layer.19.1 0.03736199 33.13145445 + layer.29.0 4.17784350 38.71202692 + layer.29.1 4.17623735 53.36130014 + layer.39.0 10.57947434 1513.51209806 + layer.39.1 10.58388675 1571.66029927 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 404.74632522 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8335116 +BPFP 0.6479 bits/point +EBPFP 0.6479 equivalent bits/point +MSE 404.746325 +---------------------- --------------------------------------------------------- +Time: 22.178s Load: 1.295s, Pack+Encode: 7.785s, Decode+Unpack: 13.098s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 404.7463 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000442-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.273s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 537,188B, BPFP=0.3340 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 532,036B, BPFP=0.3308 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,334,696B, BPFP=0.8299 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,306,056B, BPFP=0.8121 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,343,572B, BPFP=0.8355 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,337,108B, BPFP=0.8314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,004,492B, BPFP=0.6246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 956,920B, BPFP=0.5950 +⌛️ [2/4] FRONTEND: Frontend time: 7.742s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.685s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 8.26206635 + layer.9.1 0.03247583 4.47339932 + layer.19.0 0.05000294 11.00402813 + layer.19.1 0.04728991 15.24492598 + layer.29.0 4.17616118 119.68089382 + layer.29.1 4.18555745 114.48141515 + layer.39.0 14.92630606 1301.48193251 + layer.39.1 15.22664209 1396.48057943 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 371.38865509 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8352068 +BPFP 0.6492 bits/point +EBPFP 0.6492 equivalent bits/point +MSE 371.388655 +---------------------- --------------------------------------------------------- +Time: 21.701s Load: 1.273s, Pack+Encode: 7.742s, Decode+Unpack: 12.685s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 371.3887 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000447-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.275s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 535,648B, BPFP=0.3331 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 535,212B, BPFP=0.3328 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,218,304B, BPFP=0.7576 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,170,072B, BPFP=0.7276 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,224,576B, BPFP=0.7615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,202,788B, BPFP=0.7479 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 805,908B, BPFP=0.5011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 827,744B, BPFP=0.5147 +⌛️ [2/4] FRONTEND: Frontend time: 7.862s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.779s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 16.85364932 + layer.9.1 0.11516861 17.18644540 + layer.19.0 0.04822375 98.77093282 + layer.19.1 0.02465675 34.01683381 + layer.29.0 0.12445424 36.29532046 + layer.29.1 4.21809243 33.42992628 + layer.39.0 56.99443848 1517.12034384 + layer.39.1 29.63154648 1423.47150589 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 397.14311973 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7520252 +BPFP 0.5845 bits/point +EBPFP 0.5845 equivalent bits/point +MSE 397.143120 +---------------------- --------------------------------------------------------- +Time: 21.916s Load: 1.275s, Pack+Encode: 7.862s, Decode+Unpack: 12.779s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 397.1431 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000455-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 545,768B, BPFP=0.3394 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 559,872B, BPFP=0.3481 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,378,068B, BPFP=0.8569 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,336,852B, BPFP=0.8313 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,465,220B, BPFP=0.9111 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,552,536B, BPFP=0.9654 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 959,256B, BPFP=0.5965 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 978,904B, BPFP=0.6087 +⌛️ [2/4] FRONTEND: Frontend time: 7.793s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 4.58013875 + layer.9.1 0.14323425 12.34671233 + layer.19.0 0.12097352 65.59122791 + layer.19.1 0.11863553 34.14834050 + layer.29.0 0.18810310 268.54138809 + layer.29.1 0.22084548 263.61013610 + layer.39.0 11.17468934 1615.92072588 + layer.39.1 12.52284677 1663.76265521 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 491.06266560 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8776476 +BPFP 0.6822 bits/point +EBPFP 0.6822 equivalent bits/point +MSE 491.062666 +---------------------- --------------------------------------------------------- +Time: 22.552s Load: 1.304s, Pack+Encode: 7.793s, Decode+Unpack: 13.455s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 491.0627 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000474-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.299s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 549,908B, BPFP=0.3419 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 556,396B, BPFP=0.3460 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,335,136B, BPFP=0.8302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,352,192B, BPFP=0.8408 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,494,260B, BPFP=0.9292 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,483,652B, BPFP=0.9226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 989,252B, BPFP=0.6151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 986,196B, BPFP=0.6132 +⌛️ [2/4] FRONTEND: Frontend time: 7.858s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.430s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 4.35644407 + layer.9.1 0.14176414 8.27762854 + layer.19.0 0.11837582 24.58811634 + layer.19.1 0.11399856 25.06284822 + layer.29.0 0.14311602 215.37748727 + layer.29.1 0.14520382 214.45883079 + layer.39.0 14.59939236 1556.89239096 + layer.39.1 17.09091825 1691.79608405 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 467.60122878 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8746992 +BPFP 0.6799 bits/point +EBPFP 0.6799 equivalent bits/point +MSE 467.601229 +---------------------- --------------------------------------------------------- +Time: 22.587s Load: 1.299s, Pack+Encode: 7.858s, Decode+Unpack: 13.430s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 467.6012 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000476-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.317s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 513,572B, BPFP=0.3193 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 514,644B, BPFP=0.3200 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,226,928B, BPFP=0.7629 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,224,516B, BPFP=0.7614 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,388,072B, BPFP=0.8631 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,419,172B, BPFP=0.8825 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 963,684B, BPFP=0.5992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,001,480B, BPFP=0.6227 +⌛️ [2/4] FRONTEND: Frontend time: 7.883s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.995s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 4.36143508 + layer.9.1 0.14209374 4.32879625 + layer.19.0 0.05177973 6.27660565 + layer.19.1 0.05586525 20.08233221 + layer.29.0 0.12731753 85.65710562 + layer.29.1 0.12791453 75.90396669 + layer.39.0 10.91882437 1483.34495384 + layer.39.1 9.86751520 1446.60331105 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 390.81981330 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8252068 +BPFP 0.6414 bits/point +EBPFP 0.6414 equivalent bits/point +MSE 390.819813 +---------------------- --------------------------------------------------------- +Time: 22.195s Load: 1.317s, Pack+Encode: 7.883s, Decode+Unpack: 12.995s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 390.8198 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000479-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 532,660B, BPFP=0.3312 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 536,796B, BPFP=0.3338 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,413,320B, BPFP=0.8788 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,458,480B, BPFP=0.9069 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,368,196B, BPFP=0.8508 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,420,916B, BPFP=0.8835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 954,104B, BPFP=0.5933 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,000,464B, BPFP=0.6221 +⌛️ [2/4] FRONTEND: Frontend time: 7.883s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 4.30744650 + layer.9.1 0.03257298 8.25141712 + layer.19.0 0.03929411 19.59154628 + layer.19.1 0.03736255 42.69624025 + layer.29.0 4.19976128 93.27350963 + layer.29.1 4.19887364 70.41640799 + layer.39.0 17.81771704 1584.34925183 + layer.39.1 13.24929237 1571.63291945 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 424.31484238 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8684936 +BPFP 0.6751 bits/point +EBPFP 0.6751 equivalent bits/point +MSE 424.314842 +---------------------- --------------------------------------------------------- +Time: 22.157s Load: 1.252s, Pack+Encode: 7.883s, Decode+Unpack: 13.022s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 424.3148 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000489-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 598,012B, BPFP=0.3719 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 606,612B, BPFP=0.3772 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,298,100B, BPFP=0.8072 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,344,204B, BPFP=0.8358 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,515,364B, BPFP=0.9423 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,558,660B, BPFP=0.9692 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 972,696B, BPFP=0.6048 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 953,848B, BPFP=0.5931 +⌛️ [2/4] FRONTEND: Frontend time: 7.820s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 20.60642510 + layer.9.1 0.14206870 24.98748906 + layer.19.0 0.11541664 47.71789936 + layer.19.1 0.11639375 20.31168915 + layer.29.0 4.18928181 35.50399206 + layer.29.1 4.20210771 51.24760228 + layer.39.0 272.14109758 2215.12862146 + layer.39.1 217.56435053 2188.39955428 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 575.48790909 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8847496 +BPFP 0.6877 bits/point +EBPFP 0.6877 equivalent bits/point +MSE 575.487909 +---------------------- --------------------------------------------------------- +Time: 22.574s Load: 1.304s, Pack+Encode: 7.820s, Decode+Unpack: 13.450s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 575.4879 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.302s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 602,344B, BPFP=0.3745 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 601,140B, BPFP=0.3738 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,366,944B, BPFP=0.8500 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,389,636B, BPFP=0.8641 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,611,212B, BPFP=1.0019 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,660,196B, BPFP=1.0323 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,063,268B, BPFP=0.6612 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,092,184B, BPFP=0.6791 +⌛️ [2/4] FRONTEND: Frontend time: 7.964s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.345s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 12.53339154 + layer.9.1 0.14265629 12.53560147 + layer.19.0 0.15235519 165.24886581 + layer.19.1 0.14002283 162.19413602 + layer.29.0 4.20702410 99.05324737 + layer.29.1 4.22502724 108.54287050 + layer.39.0 9.71896204 1619.74546323 + layer.39.1 14.02077861 1723.93807705 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 487.97395662 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9386924 +BPFP 0.7296 bits/point +EBPFP 0.7296 equivalent bits/point +MSE 487.973957 +---------------------- --------------------------------------------------------- +Time: 22.611s Load: 1.302s, Pack+Encode: 7.964s, Decode+Unpack: 13.345s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 487.9740 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000524-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 554,204B, BPFP=0.3446 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 539,640B, BPFP=0.3356 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,300,108B, BPFP=0.8084 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,250,808B, BPFP=0.7778 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,585,916B, BPFP=0.9861 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,484,528B, BPFP=0.9231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,156,776B, BPFP=0.7193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,180,800B, BPFP=0.7342 +⌛️ [2/4] FRONTEND: Frontend time: 7.933s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.333s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 4.33146882 + layer.9.1 0.14327397 4.31117149 + layer.19.0 0.03872790 15.44850117 + layer.19.1 0.03991431 6.23037423 + layer.29.0 0.11363128 77.85670368 + layer.29.1 0.09618797 44.98972262 + layer.39.0 113.00349212 2259.19770774 + layer.39.1 66.70960681 2099.81821076 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 564.02298256 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9052780 +BPFP 0.7036 bits/point +EBPFP 0.7036 equivalent bits/point +MSE 564.022983 +---------------------- --------------------------------------------------------- +Time: 22.571s Load: 1.305s, Pack+Encode: 7.933s, Decode+Unpack: 13.333s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 564.0230 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000536-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.313s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,832B, BPFP=0.4122 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 643,056B, BPFP=0.3999 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,426,932B, BPFP=0.8873 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,399,740B, BPFP=0.8704 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,588,624B, BPFP=0.9878 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,532,272B, BPFP=0.9528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 800,696B, BPFP=0.4979 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 804,908B, BPFP=0.5005 +⌛️ [2/4] FRONTEND: Frontend time: 7.888s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.119s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 20.76878134 + layer.9.1 0.14239137 16.38379497 + layer.19.0 0.03888746 24.89808928 + layer.19.1 0.04246985 46.89799725 + layer.29.0 0.10356636 85.57157354 + layer.29.1 0.10009016 61.15549387 + layer.39.0 8.56607607 1596.52833493 + layer.39.1 7.91790657 1483.76631646 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 416.99629771 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8859060 +BPFP 0.6886 bits/point +EBPFP 0.6886 equivalent bits/point +MSE 416.996298 +---------------------- --------------------------------------------------------- +Time: 22.320s Load: 1.313s, Pack+Encode: 7.888s, Decode+Unpack: 13.119s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 416.9963 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000546-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.310s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 549,652B, BPFP=0.3418 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 572,292B, BPFP=0.3559 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,181,100B, BPFP=0.7344 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,233,084B, BPFP=0.7668 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,273,004B, BPFP=0.7916 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,356,140B, BPFP=0.8433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 812,564B, BPFP=0.5053 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 812,872B, BPFP=0.5055 +⌛️ [2/4] FRONTEND: Frontend time: 7.755s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 16.65654225 + layer.9.1 0.14243852 8.31947118 + layer.19.0 0.05701358 43.31388789 + layer.19.1 0.05730241 20.63136740 + layer.29.0 4.14713759 39.04976520 + layer.29.1 4.15440538 34.52079851 + layer.39.0 12.45677755 1591.78812480 + layer.39.1 14.71734096 1606.57784145 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 420.10722484 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7790708 +BPFP 0.6055 bits/point +EBPFP 0.6055 equivalent bits/point +MSE 420.107225 +---------------------- --------------------------------------------------------- +Time: 22.468s Load: 1.310s, Pack+Encode: 7.755s, Decode+Unpack: 13.402s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 420.1072 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000556-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.301s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 640,092B, BPFP=0.3980 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 627,124B, BPFP=0.3900 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,451,924B, BPFP=0.9028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,424,836B, BPFP=0.8860 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,792,584B, BPFP=1.1147 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,753,324B, BPFP=1.0902 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,333,600B, BPFP=0.8293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,251,608B, BPFP=0.7783 +⌛️ [2/4] FRONTEND: Frontend time: 8.085s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.540s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 45.54057724 + layer.9.1 0.11180697 52.67732211 + layer.19.0 0.09949989 20.48028594 + layer.19.1 0.11883939 29.45850744 + layer.29.0 0.15177689 335.69734957 + layer.29.1 0.14123031 291.65844874 + layer.39.0 349.58010984 2916.10951926 + layer.39.1 334.73010188 2610.01321235 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 787.70440283 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10275092 +BPFP 0.7987 bits/point +EBPFP 0.7987 equivalent bits/point +MSE 787.704403 +---------------------- --------------------------------------------------------- +Time: 22.927s Load: 1.301s, Pack+Encode: 8.085s, Decode+Unpack: 13.540s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 787.7044 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000620-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.296s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 509,532B, BPFP=0.3168 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 503,544B, BPFP=0.3131 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 981,104B, BPFP=0.6101 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 919,576B, BPFP=0.5718 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,109,876B, BPFP=0.6901 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,026,128B, BPFP=0.6381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 798,680B, BPFP=0.4966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 768,212B, BPFP=0.4777 +⌛️ [2/4] FRONTEND: Frontend time: 7.691s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.818s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72630507 20.92988897 + layer.9.1 2.71889861 8.57395137 + layer.19.0 3.15508441 21.75571325 + layer.19.1 3.14332772 7.77713233 + layer.29.0 4.15805451 46.51559515 + layer.29.1 4.14588961 68.09149156 + layer.39.0 8.22539970 1210.31072907 + layer.39.1 8.64785859 1226.66427889 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 326.32734757 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6616652 +BPFP 0.5143 bits/point +EBPFP 0.5143 equivalent bits/point +MSE 326.327348 +---------------------- --------------------------------------------------------- +Time: 21.806s Load: 1.296s, Pack+Encode: 7.691s, Decode+Unpack: 12.818s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 326.3273 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000624-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 550,656B, BPFP=0.3424 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 579,104B, BPFP=0.3601 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,314,528B, BPFP=0.8174 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,362,080B, BPFP=0.8470 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,457,432B, BPFP=0.9063 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,578,032B, BPFP=0.9812 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 904,888B, BPFP=0.5627 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 953,504B, BPFP=0.5929 +⌛️ [2/4] FRONTEND: Frontend time: 7.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 8.61601849 + layer.9.1 0.11119189 4.38237039 + layer.19.0 0.08174444 28.88375517 + layer.19.1 0.08249469 6.21726946 + layer.29.0 4.18188438 119.76099371 + layer.29.1 4.20908200 141.62015481 + layer.39.0 9.33443395 1599.76583890 + layer.39.1 9.53268950 1646.24164279 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 444.43600546 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8700224 +BPFP 0.6762 bits/point +EBPFP 0.6762 equivalent bits/point +MSE 444.436005 +---------------------- --------------------------------------------------------- +Time: 21.446s Load: 1.304s, Pack+Encode: 7.548s, Decode+Unpack: 12.595s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 444.4360 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 570,152B, BPFP=0.3545 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 582,560B, BPFP=0.3622 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,301,508B, BPFP=0.8093 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,289,808B, BPFP=0.8020 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,291,136B, BPFP=0.8028 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,285,208B, BPFP=0.7992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 839,184B, BPFP=0.5218 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 834,848B, BPFP=0.5191 +⌛️ [2/4] FRONTEND: Frontend time: 7.674s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.099s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 52.72707239 + layer.9.1 0.03285184 45.41280643 + layer.19.0 0.04037820 6.17500771 + layer.19.1 0.04362713 6.33009989 + layer.29.0 0.11518513 58.48924507 + layer.29.1 0.11703357 49.15273301 + layer.39.0 256.78569723 1650.01273480 + layer.39.1 143.16752229 1538.90990131 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 425.90120007 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7994404 +BPFP 0.6214 bits/point +EBPFP 0.6214 equivalent bits/point +MSE 425.901200 +---------------------- --------------------------------------------------------- +Time: 21.923s Load: 1.149s, Pack+Encode: 7.674s, Decode+Unpack: 13.099s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 425.9012 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000676-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 616,500B, BPFP=0.3833 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 623,396B, BPFP=0.3876 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,413,720B, BPFP=0.8791 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,377,632B, BPFP=0.8566 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,387,192B, BPFP=0.8626 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,392,476B, BPFP=0.8659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 848,724B, BPFP=0.5278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 825,532B, BPFP=0.5133 +⌛️ [2/4] FRONTEND: Frontend time: 7.491s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.177s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 30.13941121 + layer.9.1 0.11256296 53.40822986 + layer.19.0 0.03396921 38.34904041 + layer.19.1 0.04105656 15.95825250 + layer.29.0 4.20373127 70.04602933 + layer.29.1 4.19418701 58.39163483 + layer.39.0 8.83613586 1249.50350207 + layer.39.1 8.48765384 1267.61946832 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 347.92694607 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8485172 +BPFP 0.6595 bits/point +EBPFP 0.6595 equivalent bits/point +MSE 347.926946 +---------------------- --------------------------------------------------------- +Time: 21.893s Load: 1.225s, Pack+Encode: 7.491s, Decode+Unpack: 13.177s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 347.9269 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000678-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.264s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 577,740B, BPFP=0.3592 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 571,780B, BPFP=0.3555 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,349,312B, BPFP=0.8390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,392,300B, BPFP=0.8658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,513,524B, BPFP=0.9411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,486,820B, BPFP=0.9245 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 932,452B, BPFP=0.5798 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 933,768B, BPFP=0.5806 +⌛️ [2/4] FRONTEND: Frontend time: 7.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 4.35766283 + layer.9.1 0.03228644 4.29617670 + layer.19.0 0.12067159 20.32734027 + layer.19.1 0.11791951 33.18417204 + layer.29.0 0.15835167 234.47250080 + layer.29.1 0.15268422 223.94126074 + layer.39.0 158.29335801 2316.37169691 + layer.39.1 131.92238738 2305.13673989 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 642.76094377 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8757696 +BPFP 0.6807 bits/point +EBPFP 0.6807 equivalent bits/point +MSE 642.760944 +---------------------- --------------------------------------------------------- +Time: 21.975s Load: 1.264s, Pack+Encode: 7.650s, Decode+Unpack: 13.062s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 642.7609 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000684-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 549,540B, BPFP=0.3417 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 549,864B, BPFP=0.3419 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,355,112B, BPFP=0.8426 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,350,044B, BPFP=0.8395 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,380,176B, BPFP=0.8582 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,447,328B, BPFP=0.9000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 828,448B, BPFP=0.5151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 855,276B, BPFP=0.5318 +⌛️ [2/4] FRONTEND: Frontend time: 7.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 8.42857321 + layer.9.1 0.03230341 4.33665290 + layer.19.0 0.01113602 6.53508536 + layer.19.1 0.03747142 15.43096222 + layer.29.0 4.12172023 71.61852814 + layer.29.1 4.13913264 42.20228430 + layer.39.0 9.31610902 1191.23957338 + layer.39.1 11.00762596 1207.10044572 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 318.36151315 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8315788 +BPFP 0.6464 bits/point +EBPFP 0.6464 equivalent bits/point +MSE 318.361513 +---------------------- --------------------------------------------------------- +Time: 21.965s Load: 1.308s, Pack+Encode: 7.633s, Decode+Unpack: 13.024s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 318.3615 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000693-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.285s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 582,164B, BPFP=0.3620 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 560,620B, BPFP=0.3486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,335,488B, BPFP=0.8304 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,310,328B, BPFP=0.8148 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,527,808B, BPFP=0.9500 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,509,336B, BPFP=0.9385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 951,008B, BPFP=0.5914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 957,372B, BPFP=0.5953 +⌛️ [2/4] FRONTEND: Frontend time: 7.787s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.349s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14187056 4.69230104 + layer.9.1 0.14241365 12.43136019 + layer.19.0 0.11657135 38.20631616 + layer.19.1 0.11473399 60.66258755 + layer.29.0 0.16421308 111.06725565 + layer.29.1 0.18111406 201.86837393 + layer.39.0 55.30549089 2109.39796243 + layer.39.1 49.87731316 1993.14772365 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 566.43423508 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8734124 +BPFP 0.6789 bits/point +EBPFP 0.6789 equivalent bits/point +MSE 566.434235 +---------------------- --------------------------------------------------------- +Time: 22.422s Load: 1.285s, Pack+Encode: 7.787s, Decode+Unpack: 13.349s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 566.4342 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000713-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 511,540B, BPFP=0.3181 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 515,916B, BPFP=0.3208 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,341,976B, BPFP=0.8345 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,286,916B, BPFP=0.8002 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,349,568B, BPFP=0.8392 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,344,864B, BPFP=0.8363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 896,964B, BPFP=0.5577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 863,116B, BPFP=0.5367 +⌛️ [2/4] FRONTEND: Frontend time: 7.910s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.188s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 4.33909757 + layer.9.1 0.03232725 4.30955788 + layer.19.0 0.03714494 6.05302165 + layer.19.1 0.03685654 6.25155330 + layer.29.0 4.16145554 30.96802869 + layer.29.1 4.17130075 31.87181630 + layer.39.0 7.63807493 1352.62034384 + layer.39.1 7.26751532 1208.89135626 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 330.66309694 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8110860 +BPFP 0.6304 bits/point +EBPFP 0.6304 equivalent bits/point +MSE 330.663097 +---------------------- --------------------------------------------------------- +Time: 22.410s Load: 1.312s, Pack+Encode: 7.910s, Decode+Unpack: 13.188s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 330.6631 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000734-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.299s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 517,680B, BPFP=0.3219 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 538,476B, BPFP=0.3348 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,302,404B, BPFP=0.8099 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,265,764B, BPFP=0.7871 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,297,376B, BPFP=0.8067 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,284,872B, BPFP=0.7990 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 825,768B, BPFP=0.5135 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 858,124B, BPFP=0.5336 +⌛️ [2/4] FRONTEND: Frontend time: 7.700s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 28.28875657 + layer.9.1 0.14394252 12.38651853 + layer.19.0 0.03713998 24.33397754 + layer.19.1 0.11359857 159.20701011 + layer.29.0 4.20669858 38.62126164 + layer.29.1 0.11083615 46.66813913 + layer.39.0 7.41086201 1181.51743075 + layer.39.1 8.74303628 1248.62575613 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 342.45610630 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7890464 +BPFP 0.6133 bits/point +EBPFP 0.6133 equivalent bits/point +MSE 342.456106 +---------------------- --------------------------------------------------------- +Time: 22.070s Load: 1.299s, Pack+Encode: 7.700s, Decode+Unpack: 13.071s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 342.4561 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000737-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 589,756B, BPFP=0.3667 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 611,140B, BPFP=0.3800 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,375,728B, BPFP=0.8555 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,428,292B, BPFP=0.8881 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,677,376B, BPFP=1.0430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,747,000B, BPFP=1.0863 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,127,552B, BPFP=0.7011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,254,760B, BPFP=0.7802 +⌛️ [2/4] FRONTEND: Frontend time: 7.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.637s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 4.42242064 + layer.9.1 0.14198353 20.54617483 + layer.19.0 0.17418623 52.18006308 + layer.19.1 0.18921874 147.64193330 + layer.29.0 0.15243895 98.73177332 + layer.29.1 0.17994503 105.13893863 + layer.39.0 13.57905399 1577.90401146 + layer.39.1 8.80701993 1698.60108246 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 463.14579972 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9811604 +BPFP 0.7626 bits/point +EBPFP 0.7626 equivalent bits/point +MSE 463.145800 +---------------------- --------------------------------------------------------- +Time: 21.536s Load: 1.249s, Pack+Encode: 7.649s, Decode+Unpack: 12.637s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 463.1458 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000804-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.279s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 567,424B, BPFP=0.3528 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 556,612B, BPFP=0.3461 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,348,504B, BPFP=0.8385 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,347,464B, BPFP=0.8379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,650,792B, BPFP=1.0265 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,633,036B, BPFP=1.0154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,093,292B, BPFP=0.6798 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 991,044B, BPFP=0.6162 +⌛️ [2/4] FRONTEND: Frontend time: 7.920s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.482s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72336357 16.49110181 + layer.9.1 2.61637510 8.39195133 + layer.19.0 0.14860626 267.23523559 + layer.19.1 0.15499876 166.89360474 + layer.29.0 0.29089499 619.26170010 + layer.29.1 0.20993857 368.15504616 + layer.39.0 12.63850088 1914.00000000 + layer.39.1 9.97545753 1684.45240369 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 630.61013043 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9188168 +BPFP 0.7142 bits/point +EBPFP 0.7142 equivalent bits/point +MSE 630.610130 +---------------------- --------------------------------------------------------- +Time: 22.681s Load: 1.279s, Pack+Encode: 7.920s, Decode+Unpack: 13.482s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 630.6101 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000816-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.302s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 604,540B, BPFP=0.3759 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 617,648B, BPFP=0.3841 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,414,604B, BPFP=0.8796 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,428,476B, BPFP=0.8882 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,785,152B, BPFP=1.1100 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,754,680B, BPFP=1.0911 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,372,992B, BPFP=0.8537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,266,264B, BPFP=0.7874 +⌛️ [2/4] FRONTEND: Frontend time: 7.720s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.871s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 4.32445815 + layer.9.1 0.14187655 4.31589294 + layer.19.0 0.17405892 95.14025191 + layer.19.1 0.14315577 43.81785259 + layer.29.0 0.19218995 402.60442534 + layer.29.1 0.16272765 310.38685928 + layer.39.0 14.01399584 1738.01766953 + layer.39.1 9.48776763 1625.01353072 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 527.95261756 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10244356 +BPFP 0.7963 bits/point +EBPFP 0.7963 equivalent bits/point +MSE 527.952618 +---------------------- --------------------------------------------------------- +Time: 21.893s Load: 1.302s, Pack+Encode: 7.720s, Decode+Unpack: 12.871s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 527.9526 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000817-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.274s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 607,452B, BPFP=0.3777 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 553,824B, BPFP=0.3444 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,350,864B, BPFP=0.8400 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,258,884B, BPFP=0.7828 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,551,636B, BPFP=0.9648 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,440,924B, BPFP=0.8960 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,032,208B, BPFP=0.6418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 939,916B, BPFP=0.5845 +⌛️ [2/4] FRONTEND: Frontend time: 7.629s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.916s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 12.74908717 + layer.9.1 0.14252999 4.33150768 + layer.19.0 0.12443910 102.92331264 + layer.19.1 0.13256963 38.91977326 + layer.29.0 4.20758094 52.98772783 + layer.29.1 4.18155761 62.02302213 + layer.39.0 45.67507362 1710.15775231 + layer.39.1 52.99942295 1592.90289717 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 447.12438502 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8735708 +BPFP 0.6790 bits/point +EBPFP 0.6790 equivalent bits/point +MSE 447.124385 +---------------------- --------------------------------------------------------- +Time: 21.819s Load: 1.274s, Pack+Encode: 7.629s, Decode+Unpack: 12.916s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 447.1244 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000880-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.275s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 560,624B, BPFP=0.3486 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 572,072B, BPFP=0.3557 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,347,240B, BPFP=0.8377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,367,708B, BPFP=0.8505 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,433,104B, BPFP=0.8911 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,445,268B, BPFP=0.8987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 999,516B, BPFP=0.6215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 936,216B, BPFP=0.5822 +⌛️ [2/4] FRONTEND: Frontend time: 7.804s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.753s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 4.34900590 + layer.9.1 0.14194541 8.61627467 + layer.19.0 0.11782019 44.74379179 + layer.19.1 0.12099331 48.19344456 + layer.29.0 0.31534543 396.05046164 + layer.29.1 0.31351768 420.52021649 + layer.39.0 16.41217467 1702.39143585 + layer.39.1 11.15875965 1623.23559376 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 531.01252808 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8661748 +BPFP 0.6733 bits/point +EBPFP 0.6733 equivalent bits/point +MSE 531.012528 +---------------------- --------------------------------------------------------- +Time: 21.832s Load: 1.275s, Pack+Encode: 7.804s, Decode+Unpack: 12.753s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 531.0125 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 551,448B, BPFP=0.3429 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 542,912B, BPFP=0.3376 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,283,036B, BPFP=0.7978 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,196,440B, BPFP=0.7440 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,466,296B, BPFP=0.9118 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,407,936B, BPFP=0.8755 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 979,644B, BPFP=0.6092 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 926,992B, BPFP=0.5764 +⌛️ [2/4] FRONTEND: Frontend time: 7.853s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.265s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 4.33388457 + layer.9.1 0.14279503 4.75663882 + layer.19.0 0.04409784 12.02335666 + layer.19.1 0.12204415 26.16141356 + layer.29.0 0.14332971 68.82111589 + layer.29.1 0.16018698 59.81167423 + layer.39.0 8.52841700 1587.30468004 + layer.39.1 19.04729908 1547.08930277 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 413.78775832 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8354704 +BPFP 0.6494 bits/point +EBPFP 0.6494 equivalent bits/point +MSE 413.787758 +---------------------- --------------------------------------------------------- +Time: 22.394s Load: 1.276s, Pack+Encode: 7.853s, Decode+Unpack: 13.265s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 413.7878 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000892-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.301s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 582,856B, BPFP=0.3624 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 575,704B, BPFP=0.3580 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,346,676B, BPFP=0.8374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,300,588B, BPFP=0.8087 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,450,404B, BPFP=0.9019 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,412,144B, BPFP=0.8781 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 930,980B, BPFP=0.5789 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 861,196B, BPFP=0.5355 +⌛️ [2/4] FRONTEND: Frontend time: 7.732s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.269s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 4.34286795 + layer.9.1 0.03263012 4.33458318 + layer.19.0 0.05225635 33.64479117 + layer.19.1 0.04916960 39.01478430 + layer.29.0 4.19413323 99.37759670 + layer.29.1 4.20728930 74.72819663 + layer.39.0 8.98594322 1517.56574339 + layer.39.1 8.30659896 1368.21330786 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 392.65273390 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8460548 +BPFP 0.6576 bits/point +EBPFP 0.6576 equivalent bits/point +MSE 392.652734 +---------------------- --------------------------------------------------------- +Time: 22.301s Load: 1.301s, Pack+Encode: 7.732s, Decode+Unpack: 13.269s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 392.6527 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000919-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.279s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 561,460B, BPFP=0.3491 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 542,216B, BPFP=0.3372 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,323,352B, BPFP=0.8229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,324,024B, BPFP=0.8233 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,489,540B, BPFP=0.9262 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,480,540B, BPFP=0.9206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 961,360B, BPFP=0.5978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 969,232B, BPFP=0.6027 +⌛️ [2/4] FRONTEND: Frontend time: 7.800s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.832s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258133 8.58414045 + layer.9.1 0.03283905 20.57802924 + layer.19.0 0.03703246 19.77645953 + layer.19.1 0.03684524 6.19301302 + layer.29.0 0.11326863 32.37401256 + layer.29.1 0.10834243 36.81535538 + layer.39.0 11.60468402 1721.21951608 + layer.39.1 14.87000682 1667.08643744 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 439.07837046 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8651724 +BPFP 0.6725 bits/point +EBPFP 0.6725 equivalent bits/point +MSE 439.078370 +---------------------- --------------------------------------------------------- +Time: 21.911s Load: 1.279s, Pack+Encode: 7.800s, Decode+Unpack: 12.832s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 439.0784 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000925-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.296s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 551,888B, BPFP=0.3432 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 558,296B, BPFP=0.3472 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,367,608B, BPFP=0.8504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,348,912B, BPFP=0.8388 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,569,764B, BPFP=0.9761 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,556,372B, BPFP=0.9678 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 965,048B, BPFP=0.6001 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 961,576B, BPFP=0.5979 +⌛️ [2/4] FRONTEND: Frontend time: 7.445s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.942s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 8.43484236 + layer.9.1 0.11188250 4.56571572 + layer.19.0 3.25906142 65.60489295 + layer.19.1 3.26015426 83.51757004 + layer.29.0 4.19564952 142.11577722 + layer.29.1 4.21244012 150.40702603 + layer.39.0 303.99934336 2399.70168736 + layer.39.1 331.94728988 2283.37456224 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 642.21525924 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8879464 +BPFP 0.6902 bits/point +EBPFP 0.6902 equivalent bits/point +MSE 642.215259 +---------------------- --------------------------------------------------------- +Time: 21.683s Load: 1.296s, Pack+Encode: 7.445s, Decode+Unpack: 12.942s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 642.2153 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000927-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.302s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 510,380B, BPFP=0.3174 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 503,336B, BPFP=0.3130 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,233,856B, BPFP=0.7672 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,212,324B, BPFP=0.7538 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,331,112B, BPFP=0.8277 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,312,276B, BPFP=0.8160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 911,832B, BPFP=0.5670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 896,256B, BPFP=0.5573 +⌛️ [2/4] FRONTEND: Frontend time: 7.823s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 12.38433222 + layer.9.1 0.00271392 4.36646526 + layer.19.0 3.19073251 6.36458706 + layer.19.1 3.15044721 10.67341337 + layer.29.0 4.17151372 25.59382711 + layer.29.1 4.17302847 26.09299138 + layer.39.0 85.12206503 1965.66061764 + layer.39.1 85.43754975 1957.79974530 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 501.11699742 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7911372 +BPFP 0.6149 bits/point +EBPFP 0.6149 equivalent bits/point +MSE 501.116997 +---------------------- --------------------------------------------------------- +Time: 22.157s Load: 1.302s, Pack+Encode: 7.823s, Decode+Unpack: 13.032s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 501.1170 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000942-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.297s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 571,760B, BPFP=0.3555 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 560,744B, BPFP=0.3487 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,357,020B, BPFP=0.8438 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,369,060B, BPFP=0.8513 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,537,756B, BPFP=0.9562 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,433,600B, BPFP=0.8914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,086,816B, BPFP=0.6758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,101,760B, BPFP=0.6851 +⌛️ [2/4] FRONTEND: Frontend time: 8.019s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.428s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 8.32248202 + layer.9.1 2.75948239 20.36424258 + layer.19.0 0.15224024 56.75562122 + layer.19.1 0.13045117 47.83961119 + layer.29.0 0.13097460 278.00879497 + layer.29.1 0.13177276 254.72538602 + layer.39.0 10.49186664 1758.01528176 + layer.39.1 12.55703299 1657.82839860 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 510.23247729 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9018516 +BPFP 0.7010 bits/point +EBPFP 0.7010 equivalent bits/point +MSE 510.232477 +---------------------- --------------------------------------------------------- +Time: 22.743s Load: 1.297s, Pack+Encode: 8.019s, Decode+Unpack: 13.428s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 510.2325 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000946-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.293s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 544,524B, BPFP=0.3386 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 543,848B, BPFP=0.3382 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,281,672B, BPFP=0.7970 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,371,564B, BPFP=0.8529 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,323,608B, BPFP=0.8230 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,339,752B, BPFP=0.8331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 879,612B, BPFP=0.5470 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 881,036B, BPFP=0.5478 +⌛️ [2/4] FRONTEND: Frontend time: 7.873s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.386s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 4.36406691 + layer.9.1 0.03228249 4.34407894 + layer.19.0 0.04154089 66.91537826 + layer.19.1 0.04120101 84.52060450 + layer.29.0 4.21417063 72.79030862 + layer.29.1 4.21428318 54.14600744 + layer.39.0 28.58093312 1602.33651703 + layer.39.1 17.10356972 1519.79114932 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 426.15101388 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8165616 +BPFP 0.6347 bits/point +EBPFP 0.6347 equivalent bits/point +MSE 426.151014 +---------------------- --------------------------------------------------------- +Time: 22.551s Load: 1.293s, Pack+Encode: 7.873s, Decode+Unpack: 13.386s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 426.1510 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000959-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 589,440B, BPFP=0.3665 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 587,788B, BPFP=0.3655 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,367,764B, BPFP=0.8505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,369,356B, BPFP=0.8515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,589,568B, BPFP=0.9884 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,592,900B, BPFP=0.9905 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,084,108B, BPFP=0.6741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,084,216B, BPFP=0.6742 +⌛️ [2/4] FRONTEND: Frontend time: 7.586s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.623s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 4.69634285 + layer.9.1 0.14242138 28.41774614 + layer.19.0 0.13512425 71.18729604 + layer.19.1 0.13152432 56.68268465 + layer.29.0 0.11439834 106.33915353 + layer.29.1 0.11806111 125.36926934 + layer.39.0 18.41482236 1798.29337791 + layer.39.1 20.38586935 1866.18035657 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 507.14577838 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9265140 +BPFP 0.7202 bits/point +EBPFP 0.7202 equivalent bits/point +MSE 507.145778 +---------------------- --------------------------------------------------------- +Time: 21.518s Load: 1.309s, Pack+Encode: 7.586s, Decode+Unpack: 12.623s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 507.1458 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000972-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.253s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 545,800B, BPFP=0.3394 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 533,164B, BPFP=0.3315 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,335,404B, BPFP=0.8304 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,308,336B, BPFP=0.8135 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,355,780B, BPFP=0.8430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,314,560B, BPFP=0.8174 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,011,836B, BPFP=0.6292 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 915,904B, BPFP=0.5695 +⌛️ [2/4] FRONTEND: Frontend time: 7.840s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.739s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 8.22394154 + layer.9.1 0.14251336 4.42375195 + layer.19.0 0.11881898 47.67817773 + layer.19.1 0.11371834 52.66679103 + layer.29.0 0.15377442 132.43693290 + layer.29.1 0.16319071 130.91715417 + layer.39.0 9.10150218 1465.74164279 + layer.39.1 9.15265777 1493.95288125 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 417.00515917 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8320784 +BPFP 0.6467 bits/point +EBPFP 0.6467 equivalent bits/point +MSE 417.005159 +---------------------- --------------------------------------------------------- +Time: 21.832s Load: 1.253s, Pack+Encode: 7.840s, Decode+Unpack: 12.739s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 417.0052 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001000-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 518,760B, BPFP=0.3226 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 516,888B, BPFP=0.3214 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,303,652B, BPFP=0.8106 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,241,228B, BPFP=0.7718 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,153,740B, BPFP=0.7174 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,157,788B, BPFP=0.7199 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 892,812B, BPFP=0.5552 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 909,844B, BPFP=0.5658 +⌛️ [2/4] FRONTEND: Frontend time: 7.889s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.185s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 4.44398554 + layer.9.1 0.14223260 4.44545272 + layer.19.0 0.05715554 44.39957418 + layer.19.1 0.06015340 79.55867956 + layer.29.0 0.19165729 189.43616683 + layer.29.1 0.21090307 254.01689351 + layer.39.0 19.07211701 1479.38650111 + layer.39.1 16.66110887 1496.91085642 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 444.07476373 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7694712 +BPFP 0.5981 bits/point +EBPFP 0.5981 equivalent bits/point +MSE 444.074764 +---------------------- --------------------------------------------------------- +Time: 22.363s Load: 1.289s, Pack+Encode: 7.889s, Decode+Unpack: 13.185s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 444.0748 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 586,948B, BPFP=0.3650 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 571,932B, BPFP=0.3556 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,344,964B, BPFP=0.8363 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,342,952B, BPFP=0.8351 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,634,472B, BPFP=1.0163 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,577,160B, BPFP=0.9807 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,084,704B, BPFP=0.6745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 994,308B, BPFP=0.6183 +⌛️ [2/4] FRONTEND: Frontend time: 7.895s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.954s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 4.31691676 + layer.9.1 0.14288678 8.29992874 + layer.19.0 0.11144568 42.71777499 + layer.19.1 0.11742487 33.76766704 + layer.29.0 0.11418290 72.94886183 + layer.29.1 0.10734091 52.04226858 + layer.39.0 54.48020137 2283.20296084 + layer.39.1 66.40954314 2081.03454314 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 572.29136524 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9137440 +BPFP 0.7102 bits/point +EBPFP 0.7102 equivalent bits/point +MSE 572.291365 +---------------------- --------------------------------------------------------- +Time: 22.092s Load: 1.243s, Pack+Encode: 7.895s, Decode+Unpack: 12.954s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.2914 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001056-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.274s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 532,384B, BPFP=0.3310 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 523,596B, BPFP=0.3256 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,317,204B, BPFP=0.8191 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,330,832B, BPFP=0.8275 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,391,620B, BPFP=0.8653 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,328,300B, BPFP=0.8260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,016,376B, BPFP=0.6320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 946,404B, BPFP=0.5885 +⌛️ [2/4] FRONTEND: Frontend time: 8.031s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.347s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 4.40291924 + layer.9.1 0.00081411 4.40490688 + layer.19.0 0.01015774 19.85304118 + layer.19.1 3.16362350 6.44538152 + layer.29.0 4.19769406 39.60232112 + layer.29.1 4.18061463 34.70408508 + layer.39.0 8.41366640 1485.21092009 + layer.39.1 8.38033145 1474.42597899 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 383.63119426 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8386716 +BPFP 0.6519 bits/point +EBPFP 0.6519 equivalent bits/point +MSE 383.631194 +---------------------- --------------------------------------------------------- +Time: 22.653s Load: 1.274s, Pack+Encode: 8.031s, Decode+Unpack: 13.347s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 383.6312 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001074-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.309s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 494,484B, BPFP=0.3075 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 501,884B, BPFP=0.3121 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,281,620B, BPFP=0.7969 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,355,336B, BPFP=0.8428 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,368,500B, BPFP=0.8510 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,402,248B, BPFP=0.8719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 913,328B, BPFP=0.5679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 922,360B, BPFP=0.5735 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.403s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 4.35053433 + layer.9.1 0.03271215 4.35819355 + layer.19.0 3.19210144 10.80453826 + layer.19.1 3.19171965 6.27926268 + layer.29.0 0.11530653 52.84888372 + layer.29.1 0.10966549 89.93104306 + layer.39.0 16.12381606 1459.02690226 + layer.39.1 25.33235335 1743.90846864 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 421.43847831 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8239760 +BPFP 0.6405 bits/point +EBPFP 0.6405 equivalent bits/point +MSE 421.438478 +---------------------- --------------------------------------------------------- +Time: 22.590s Load: 1.309s, Pack+Encode: 7.878s, Decode+Unpack: 13.403s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 421.4385 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001078-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 500,380B, BPFP=0.3111 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 518,140B, BPFP=0.3222 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,398,548B, BPFP=0.8696 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,386,616B, BPFP=0.8622 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,304,160B, BPFP=0.8109 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,467,268B, BPFP=0.9124 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 907,480B, BPFP=0.5643 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 956,032B, BPFP=0.5945 +⌛️ [2/4] FRONTEND: Frontend time: 8.018s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.380s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64207787 4.37202088 + layer.9.1 0.03100527 4.36687410 + layer.19.0 3.19321449 6.31750562 + layer.19.1 3.20089330 6.11645438 + layer.29.0 0.10652387 103.49583134 + layer.29.1 0.17364564 482.98527539 + layer.39.0 9.89558772 1556.40815027 + layer.39.1 12.87769495 1803.87488061 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 495.99212407 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8438624 +BPFP 0.6559 bits/point +EBPFP 0.6559 equivalent bits/point +MSE 495.992124 +---------------------- --------------------------------------------------------- +Time: 22.704s Load: 1.306s, Pack+Encode: 8.018s, Decode+Unpack: 13.380s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 495.9921 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001086-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 499,872B, BPFP=0.3108 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 513,020B, BPFP=0.3190 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,314,464B, BPFP=0.8174 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,354,148B, BPFP=0.8420 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,425,024B, BPFP=0.8861 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,452,348B, BPFP=0.9031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 956,068B, BPFP=0.5945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 972,412B, BPFP=0.6047 +⌛️ [2/4] FRONTEND: Frontend time: 7.900s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.409s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 4.30030649 + layer.9.1 0.03183258 4.32400827 + layer.19.0 0.03873757 15.07202125 + layer.19.1 0.03841183 10.49371717 + layer.29.0 0.10242378 65.84323265 + layer.29.1 0.10979955 133.75650669 + layer.39.0 11.55027136 1784.87233365 + layer.39.1 12.74680635 1907.05555556 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 490.71471021 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8487356 +BPFP 0.6597 bits/point +EBPFP 0.6597 equivalent bits/point +MSE 490.714710 +---------------------- --------------------------------------------------------- +Time: 22.615s Load: 1.305s, Pack+Encode: 7.900s, Decode+Unpack: 13.409s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 490.7147 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001102-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.295s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 582,008B, BPFP=0.3619 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 576,336B, BPFP=0.3584 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,395,920B, BPFP=0.8680 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,398,224B, BPFP=0.8694 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,657,500B, BPFP=1.0307 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,675,628B, BPFP=1.0419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,092,736B, BPFP=0.6795 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,104,308B, BPFP=0.6867 +⌛️ [2/4] FRONTEND: Frontend time: 7.717s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.625s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 8.27148251 + layer.9.1 0.03112686 8.28997097 + layer.19.0 0.03695946 10.84359082 + layer.19.1 0.03932408 10.75832861 + layer.29.0 0.11080087 140.11340934 + layer.29.1 0.12351766 122.11249403 + layer.39.0 27.63217079 1850.41006049 + layer.39.1 35.42625259 1992.38729704 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 517.89832923 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9482660 +BPFP 0.7371 bits/point +EBPFP 0.7371 equivalent bits/point +MSE 517.898329 +---------------------- --------------------------------------------------------- +Time: 21.637s Load: 1.295s, Pack+Encode: 7.717s, Decode+Unpack: 12.625s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 517.8983 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001107-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.303s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 541,516B, BPFP=0.3367 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 536,076B, BPFP=0.3333 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,374,456B, BPFP=0.8547 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,371,736B, BPFP=0.8530 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,530,024B, BPFP=0.9514 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,523,640B, BPFP=0.9474 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,015,260B, BPFP=0.6313 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,001,820B, BPFP=0.6229 +⌛️ [2/4] FRONTEND: Frontend time: 7.872s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.048s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 4.36974192 + layer.9.1 0.11126176 4.39914793 + layer.19.0 0.00622823 5.97961873 + layer.19.1 0.00986777 6.04435291 + layer.29.0 4.20227933 115.15075812 + layer.29.1 4.19170939 76.41123448 + layer.39.0 64.89367936 1574.01942057 + layer.39.1 48.85537050 1710.66602993 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 437.13003807 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8894528 +BPFP 0.6913 bits/point +EBPFP 0.6913 equivalent bits/point +MSE 437.130038 +---------------------- --------------------------------------------------------- +Time: 22.223s Load: 1.303s, Pack+Encode: 7.872s, Decode+Unpack: 13.048s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 437.1300 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001116-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.274s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 543,744B, BPFP=0.3381 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 550,164B, BPFP=0.3421 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,342,204B, BPFP=0.8346 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,309,212B, BPFP=0.8141 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,454,988B, BPFP=0.9047 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,482,520B, BPFP=0.9219 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 919,236B, BPFP=0.5716 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 959,888B, BPFP=0.5969 +⌛️ [2/4] FRONTEND: Frontend time: 7.925s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.139s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 4.34215504 + layer.9.1 0.03110840 4.35847337 + layer.19.0 0.11193399 6.28634889 + layer.19.1 0.11167925 33.82583920 + layer.29.0 0.13638519 182.64410220 + layer.29.1 0.13233996 155.59812759 + layer.39.0 10.36537055 1495.96068131 + layer.39.1 10.25938570 1535.90512576 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 427.36510667 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8561956 +BPFP 0.6655 bits/point +EBPFP 0.6655 equivalent bits/point +MSE 427.365107 +---------------------- --------------------------------------------------------- +Time: 22.338s Load: 1.274s, Pack+Encode: 7.925s, Decode+Unpack: 13.139s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 427.3651 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001125-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.304s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 546,596B, BPFP=0.3399 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 565,296B, BPFP=0.3515 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,343,592B, BPFP=0.8355 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,327,864B, BPFP=0.8257 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,436,268B, BPFP=0.8931 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,486,164B, BPFP=0.9241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 992,300B, BPFP=0.6170 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,024,808B, BPFP=0.6372 +⌛️ [2/4] FRONTEND: Frontend time: 7.493s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.228s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14239891 4.33822703 + layer.9.1 0.14185137 4.28330759 + layer.19.0 0.03937967 20.15634452 + layer.19.1 0.04081462 20.08474610 + layer.29.0 4.18784542 80.22280922 + layer.29.1 4.19318340 51.76338646 + layer.39.0 9.46241929 1490.04775549 + layer.39.1 9.25020271 1483.69659344 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 394.32414623 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8722888 +BPFP 0.6780 bits/point +EBPFP 0.6780 equivalent bits/point +MSE 394.324146 +---------------------- --------------------------------------------------------- +Time: 22.025s Load: 1.304s, Pack+Encode: 7.493s, Decode+Unpack: 13.228s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 394.3241 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001139-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.308s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 555,308B, BPFP=0.3453 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 562,108B, BPFP=0.3495 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,330,720B, BPFP=0.8275 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,360,448B, BPFP=0.8459 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,413,196B, BPFP=0.8787 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,437,892B, BPFP=0.8941 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 890,088B, BPFP=0.5535 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 889,744B, BPFP=0.5533 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.766s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 4.28869594 + layer.9.1 0.14180939 4.29400874 + layer.19.0 0.04123239 11.23552412 + layer.19.1 0.03889530 15.72163398 + layer.29.0 0.17016378 156.47898758 + layer.29.1 0.15026704 83.35852436 + layer.39.0 12.11620503 1603.80054123 + layer.39.1 10.53236554 1630.31852913 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 438.68705563 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8439504 +BPFP 0.6560 bits/point +EBPFP 0.6560 equivalent bits/point +MSE 438.687056 +---------------------- --------------------------------------------------------- +Time: 21.952s Load: 1.308s, Pack+Encode: 7.878s, Decode+Unpack: 12.766s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 438.6871 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001145-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.293s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 563,548B, BPFP=0.3504 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 546,424B, BPFP=0.3398 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,319,892B, BPFP=0.8207 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,259,008B, BPFP=0.7829 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,528,596B, BPFP=0.9505 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,469,204B, BPFP=0.9136 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 951,468B, BPFP=0.5916 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 981,556B, BPFP=0.6103 +⌛️ [2/4] FRONTEND: Frontend time: 7.795s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.354s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 24.47743802 + layer.9.1 0.11141965 4.51032402 + layer.19.0 0.02960617 78.77241523 + layer.19.1 0.09893673 33.81552949 + layer.29.0 0.11288278 59.85616643 + layer.29.1 0.12156463 64.47690823 + layer.39.0 13.31952528 1903.09821713 + layer.39.1 8.92088009 1676.06860872 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 480.63445091 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8619696 +BPFP 0.6700 bits/point +EBPFP 0.6700 equivalent bits/point +MSE 480.634451 +---------------------- --------------------------------------------------------- +Time: 22.442s Load: 1.293s, Pack+Encode: 7.795s, Decode+Unpack: 13.354s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 480.6345 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001171-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.311s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 552,752B, BPFP=0.3437 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 553,584B, BPFP=0.3442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,398,328B, BPFP=0.8695 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,356,604B, BPFP=0.8436 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,572,548B, BPFP=0.9778 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,587,128B, BPFP=0.9869 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 953,456B, BPFP=0.5929 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 925,800B, BPFP=0.5757 +⌛️ [2/4] FRONTEND: Frontend time: 7.808s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.692s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 4.35029586 + layer.9.1 0.03269095 4.33385659 + layer.19.0 0.03939078 29.43192106 + layer.19.1 0.03751187 11.10167194 + layer.29.0 0.14354374 254.26981853 + layer.29.1 0.12315212 150.93562958 + layer.39.0 10.67588198 1673.97421203 + layer.39.1 12.04857131 1540.93282394 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 458.66627869 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8900200 +BPFP 0.6918 bits/point +EBPFP 0.6918 equivalent bits/point +MSE 458.666279 +---------------------- --------------------------------------------------------- +Time: 21.811s Load: 1.311s, Pack+Encode: 7.808s, Decode+Unpack: 12.692s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 458.6663 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001179-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 553,788B, BPFP=0.3444 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 542,060B, BPFP=0.3371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,428,832B, BPFP=0.8885 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,408,396B, BPFP=0.8758 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,520,324B, BPFP=0.9454 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,502,524B, BPFP=0.9343 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 954,556B, BPFP=0.5936 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 929,660B, BPFP=0.5781 +⌛️ [2/4] FRONTEND: Frontend time: 7.896s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.099s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 4.35836144 + layer.9.1 0.03246013 4.34446416 + layer.19.0 0.05054442 83.31060470 + layer.19.1 0.04990058 34.64597759 + layer.29.0 4.26185866 220.44382760 + layer.29.1 4.26378007 229.46491961 + layer.39.0 11.04594849 1660.47962432 + layer.39.1 9.19037403 1697.01703279 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 491.75810153 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8840140 +BPFP 0.6871 bits/point +EBPFP 0.6871 equivalent bits/point +MSE 491.758102 +---------------------- --------------------------------------------------------- +Time: 22.300s Load: 1.306s, Pack+Encode: 7.896s, Decode+Unpack: 13.099s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 491.7581 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001184-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.318s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 569,228B, BPFP=0.3540 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 557,080B, BPFP=0.3464 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,330,428B, BPFP=0.8273 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,295,468B, BPFP=0.8055 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,522,356B, BPFP=0.9466 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,457,156B, BPFP=0.9061 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,094,888B, BPFP=0.6808 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,011,308B, BPFP=0.6288 +⌛️ [2/4] FRONTEND: Frontend time: 7.887s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.142s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 4.31552389 + layer.9.1 0.14317998 4.36355050 + layer.19.0 0.15093802 42.84134730 + layer.19.1 0.13472426 15.97932088 + layer.29.0 0.10723148 97.17762058 + layer.29.1 0.10832139 57.75865568 + layer.39.0 40.62415433 1740.13132760 + layer.39.1 9.85226018 1646.46688953 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 451.12927950 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8837912 +BPFP 0.6869 bits/point +EBPFP 0.6869 equivalent bits/point +MSE 451.129279 +---------------------- --------------------------------------------------------- +Time: 22.347s Load: 1.318s, Pack+Encode: 7.887s, Decode+Unpack: 13.142s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 451.1293 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001198-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 536,708B, BPFP=0.3337 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 545,200B, BPFP=0.3390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,332,680B, BPFP=0.8287 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,306,188B, BPFP=0.8122 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,426,384B, BPFP=0.8869 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,462,920B, BPFP=0.9097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,014,584B, BPFP=0.6309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 997,716B, BPFP=0.6204 +⌛️ [2/4] FRONTEND: Frontend time: 7.704s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.714s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 4.36530464 + layer.9.1 0.03106517 4.37508363 + layer.19.0 0.04795660 66.35719118 + layer.19.1 0.11462555 52.02531539 + layer.29.0 4.19919699 169.84039717 + layer.29.1 4.19569772 166.21217367 + layer.39.0 34.63583701 1583.72190385 + layer.39.1 33.06685271 1665.67924228 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 464.07207648 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8622380 +BPFP 0.6702 bits/point +EBPFP 0.6702 equivalent bits/point +MSE 464.072076 +---------------------- --------------------------------------------------------- +Time: 21.710s Load: 1.291s, Pack+Encode: 7.704s, Decode+Unpack: 12.714s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 464.0721 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001272-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.320s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 556,440B, BPFP=0.3460 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 566,240B, BPFP=0.3521 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,337,904B, BPFP=0.8319 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,364,444B, BPFP=0.8484 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,610,040B, BPFP=1.0011 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,663,452B, BPFP=1.0344 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,003,304B, BPFP=0.6239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,074,544B, BPFP=0.6682 +⌛️ [2/4] FRONTEND: Frontend time: 7.686s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.938s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 20.49407409 + layer.9.1 0.14287666 4.31980292 + layer.19.0 0.11209038 20.15296930 + layer.19.1 0.11164490 10.85985877 + layer.29.0 0.12578187 263.94010665 + layer.29.1 0.11401374 204.23079831 + layer.39.0 22.42121339 1916.29528812 + layer.39.1 25.87191330 1900.56574339 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 542.60733020 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9176368 +BPFP 0.7133 bits/point +EBPFP 0.7133 equivalent bits/point +MSE 542.607330 +---------------------- --------------------------------------------------------- +Time: 21.945s Load: 1.320s, Pack+Encode: 7.686s, Decode+Unpack: 12.938s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 542.6073 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001342-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.285s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 523,788B, BPFP=0.3257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 543,244B, BPFP=0.3378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,349,324B, BPFP=0.8390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,338,064B, BPFP=0.8320 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,391,244B, BPFP=0.8651 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,447,680B, BPFP=0.9002 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 945,552B, BPFP=0.5880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 948,400B, BPFP=0.5897 +⌛️ [2/4] FRONTEND: Frontend time: 7.900s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.194s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 4.39184656 + layer.9.1 0.00120738 4.39209746 + layer.19.0 0.01953576 20.02368001 + layer.19.1 0.08568942 24.56802173 + layer.29.0 0.14491542 139.16091611 + layer.29.1 0.15694472 129.04899912 + layer.39.0 8.88920166 1331.14422159 + layer.39.1 9.38273353 1466.30181471 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 389.87894966 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8487296 +BPFP 0.6597 bits/point +EBPFP 0.6597 equivalent bits/point +MSE 389.878950 +---------------------- --------------------------------------------------------- +Time: 22.379s Load: 1.285s, Pack+Encode: 7.900s, Decode+Unpack: 13.194s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 389.8789 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001421-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.296s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 685,588B, BPFP=0.4263 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 665,504B, BPFP=0.4138 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,390,984B, BPFP=0.8649 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,387,828B, BPFP=0.8630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,620,912B, BPFP=1.0079 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,590,044B, BPFP=0.9887 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,073,304B, BPFP=0.6674 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,070,968B, BPFP=0.6659 +⌛️ [2/4] FRONTEND: Frontend time: 7.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.369s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 111.82300621 + layer.9.1 0.14739036 126.39890759 + layer.19.0 0.16044666 427.44949857 + layer.19.1 0.14398357 242.66849729 + layer.29.0 0.50679369 392.90178287 + layer.29.1 0.43405572 411.15874721 + layer.39.0 123.83094556 1815.26870423 + layer.39.1 72.08861628 1790.66332378 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 664.79155847 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9485132 +BPFP 0.7373 bits/point +EBPFP 0.7373 equivalent bits/point +MSE 664.791558 +---------------------- --------------------------------------------------------- +Time: 22.259s Load: 1.296s, Pack+Encode: 7.593s, Decode+Unpack: 13.369s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 664.7916 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001428-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 535,368B, BPFP=0.3329 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 547,688B, BPFP=0.3406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,227,472B, BPFP=0.7633 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,264,084B, BPFP=0.7860 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,156,048B, BPFP=0.7188 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,161,856B, BPFP=0.7225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 858,680B, BPFP=0.5339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 864,568B, BPFP=0.5376 +⌛️ [2/4] FRONTEND: Frontend time: 7.886s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 4.57767729 + layer.9.1 0.14229169 12.32070798 + layer.19.0 0.04567823 43.01054600 + layer.19.1 0.04432558 47.81525092 + layer.29.0 0.11507784 73.67754596 + layer.29.1 0.11363094 49.61701588 + layer.39.0 38.15331751 1486.19563833 + layer.39.1 50.78157832 1636.44476281 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 419.20739315 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7615764 +BPFP 0.5920 bits/point +EBPFP 0.5920 equivalent bits/point +MSE 419.207393 +---------------------- --------------------------------------------------------- +Time: 22.113s Load: 1.252s, Pack+Encode: 7.886s, Decode+Unpack: 12.975s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 419.2074 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001439-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,440B, BPFP=0.3864 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 647,756B, BPFP=0.4028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,297,044B, BPFP=0.8065 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,332,868B, BPFP=0.8288 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,441,768B, BPFP=0.8965 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,506,612B, BPFP=0.9368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 818,796B, BPFP=0.5091 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 817,788B, BPFP=0.5085 +⌛️ [2/4] FRONTEND: Frontend time: 7.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.556s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 81.99977615 + layer.9.1 0.14417255 81.23666328 + layer.19.0 0.04986641 33.98661105 + layer.19.1 0.03935205 34.16294821 + layer.29.0 4.19438972 38.77694206 + layer.29.1 0.10069272 48.91770137 + layer.39.0 8.54645341 1419.69086278 + layer.39.1 8.58293537 1472.06988220 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 401.35517339 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8484072 +BPFP 0.6594 bits/point +EBPFP 0.6594 equivalent bits/point +MSE 401.355173 +---------------------- --------------------------------------------------------- +Time: 21.464s Load: 1.276s, Pack+Encode: 7.633s, Decode+Unpack: 12.556s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 401.3552 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001452-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.280s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 540,096B, BPFP=0.3358 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 538,904B, BPFP=0.3351 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,356,976B, BPFP=0.8438 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,353,916B, BPFP=0.8419 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,517,200B, BPFP=0.9434 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,552,744B, BPFP=0.9655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 938,952B, BPFP=0.5839 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 970,404B, BPFP=0.6034 +⌛️ [2/4] FRONTEND: Frontend time: 8.043s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.417s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 4.29842768 + layer.9.1 0.14191958 16.26314147 + layer.19.0 0.11064845 16.03066549 + layer.19.1 0.11258393 33.63724729 + layer.29.0 0.14067722 239.82640879 + layer.29.1 0.15898021 263.14605221 + layer.39.0 18.90648132 1579.20566699 + layer.39.1 12.01175482 1656.66443808 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 476.13400600 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8769192 +BPFP 0.6816 bits/point +EBPFP 0.6816 equivalent bits/point +MSE 476.134006 +---------------------- --------------------------------------------------------- +Time: 22.741s Load: 1.280s, Pack+Encode: 8.043s, Decode+Unpack: 13.417s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 476.1340 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001464-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 541,584B, BPFP=0.3368 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 542,156B, BPFP=0.3371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,353,208B, BPFP=0.8414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,323,696B, BPFP=0.8231 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,516,080B, BPFP=0.9427 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,504,608B, BPFP=0.9356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 976,436B, BPFP=0.6072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 981,496B, BPFP=0.6103 +⌛️ [2/4] FRONTEND: Frontend time: 7.983s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.081s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 4.34222842 + layer.9.1 0.03265336 4.34917162 + layer.19.0 0.11338584 5.90627798 + layer.19.1 0.11737041 29.10830548 + layer.29.0 0.14518043 291.90383238 + layer.29.1 0.15176190 180.56725565 + layer.39.0 10.84722720 1578.62384591 + layer.39.1 10.76635501 1483.12973575 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 447.24133165 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8739264 +BPFP 0.6793 bits/point +EBPFP 0.6793 equivalent bits/point +MSE 447.241332 +---------------------- --------------------------------------------------------- +Time: 22.369s Load: 1.305s, Pack+Encode: 7.983s, Decode+Unpack: 13.081s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 447.2413 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001478-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.277s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 576,184B, BPFP=0.3583 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 581,292B, BPFP=0.3615 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,380,356B, BPFP=0.8583 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,338,516B, BPFP=0.8323 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,529,748B, BPFP=0.9512 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,504,404B, BPFP=0.9355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 968,760B, BPFP=0.6024 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 997,272B, BPFP=0.6201 +⌛️ [2/4] FRONTEND: Frontend time: 7.926s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.698s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 16.44966397 + layer.9.1 0.14310633 4.34819132 + layer.19.0 0.11868409 69.89343561 + layer.19.1 0.12162521 43.53451329 + layer.29.0 0.16395149 285.25073623 + layer.29.1 0.12259847 98.18479386 + layer.39.0 330.19024594 2812.28844317 + layer.39.1 213.90321554 2407.50604903 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 717.18197831 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8876532 +BPFP 0.6899 bits/point +EBPFP 0.6899 equivalent bits/point +MSE 717.181978 +---------------------- --------------------------------------------------------- +Time: 21.900s Load: 1.277s, Pack+Encode: 7.926s, Decode+Unpack: 12.698s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 717.1820 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001495-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 552,704B, BPFP=0.3437 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 567,636B, BPFP=0.3530 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,362,744B, BPFP=0.8474 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,397,228B, BPFP=0.8688 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,326,216B, BPFP=0.8247 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,392,024B, BPFP=0.8656 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 861,860B, BPFP=0.5359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 860,828B, BPFP=0.5353 +⌛️ [2/4] FRONTEND: Frontend time: 7.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.277s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 44.56248010 + layer.9.1 0.14187113 48.77846924 + layer.19.0 0.03719415 11.55414901 + layer.19.1 0.03715970 33.50182068 + layer.29.0 0.14992467 151.57075772 + layer.29.1 0.21581549 203.85070439 + layer.39.0 54.12547258 1440.25851640 + layer.39.1 37.28096148 1537.24227953 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 433.91489713 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8321240 +BPFP 0.6468 bits/point +EBPFP 0.6468 equivalent bits/point +MSE 433.914897 +---------------------- --------------------------------------------------------- +Time: 22.200s Load: 1.306s, Pack+Encode: 7.618s, Decode+Unpack: 13.277s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 433.9149 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.256s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 645,492B, BPFP=0.4014 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 643,884B, BPFP=0.4004 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,397,552B, BPFP=0.8690 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,401,812B, BPFP=0.8717 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,650,084B, BPFP=1.0260 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,647,748B, BPFP=1.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,109,844B, BPFP=0.6901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,048,508B, BPFP=0.6520 +⌛️ [2/4] FRONTEND: Frontend time: 7.966s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.475s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 24.34073295 + layer.9.1 0.14222666 65.03433918 + layer.19.0 0.12883153 112.16176178 + layer.19.1 0.12450899 153.72011302 + layer.29.0 0.12456659 174.50612862 + layer.29.1 0.12180437 146.24761223 + layer.39.0 16.93397679 1940.23193251 + layer.39.1 11.63264585 1917.46736708 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 566.71374842 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9544924 +BPFP 0.7419 bits/point +EBPFP 0.7419 equivalent bits/point +MSE 566.713748 +---------------------- --------------------------------------------------------- +Time: 22.698s Load: 1.256s, Pack+Encode: 7.966s, Decode+Unpack: 13.475s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 566.7137 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001520-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.282s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 539,384B, BPFP=0.3354 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 543,372B, BPFP=0.3379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,186,664B, BPFP=0.7379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,186,572B, BPFP=0.7378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,214,012B, BPFP=0.7549 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,216,220B, BPFP=0.7563 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 825,224B, BPFP=0.5131 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 839,076B, BPFP=0.5218 +⌛️ [2/4] FRONTEND: Frontend time: 7.841s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.766s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 28.34689390 + layer.9.1 0.14320703 20.37644013 + layer.19.0 0.18609190 127.49039916 + layer.19.1 0.20413370 157.95258278 + layer.29.0 0.16595908 113.55203359 + layer.29.1 0.17797341 161.66419930 + layer.39.0 9.44991518 1436.62702961 + layer.39.1 9.33992148 1443.40830946 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 436.17723599 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7550524 +BPFP 0.5869 bits/point +EBPFP 0.5869 equivalent bits/point +MSE 436.177236 +---------------------- --------------------------------------------------------- +Time: 21.890s Load: 1.282s, Pack+Encode: 7.841s, Decode+Unpack: 12.766s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 436.1772 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001571-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 570,112B, BPFP=0.3545 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 564,236B, BPFP=0.3509 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,156,268B, BPFP=0.7190 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,153,872B, BPFP=0.7175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,306,136B, BPFP=0.8122 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,312,724B, BPFP=0.8163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 839,492B, BPFP=0.5220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 820,788B, BPFP=0.5104 +⌛️ [2/4] FRONTEND: Frontend time: 7.858s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 16.37046572 + layer.9.1 0.14264699 4.63464033 + layer.19.0 0.04840791 21.37658688 + layer.19.1 0.04358378 48.86754815 + layer.29.0 4.25626169 60.14309734 + layer.29.1 4.25716892 99.94613578 + layer.39.0 36.32893585 1310.55969436 + layer.39.1 22.75239275 1304.16817892 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 358.25829344 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7723628 +BPFP 0.6003 bits/point +EBPFP 0.6003 equivalent bits/point +MSE 358.258293 +---------------------- --------------------------------------------------------- +Time: 22.149s Load: 1.276s, Pack+Encode: 7.858s, Decode+Unpack: 13.014s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 358.2583 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001605-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.280s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 617,928B, BPFP=0.3842 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 631,384B, BPFP=0.3926 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,427,196B, BPFP=0.8875 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,425,360B, BPFP=0.8863 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,546,032B, BPFP=0.9613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,534,324B, BPFP=0.9541 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,165,436B, BPFP=0.7247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,126,664B, BPFP=0.7006 +⌛️ [2/4] FRONTEND: Frontend time: 7.829s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.207s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 13.12451996 + layer.9.1 0.14259219 9.12482714 + layer.19.0 0.15398767 216.20095113 + layer.19.1 0.14449470 139.66376154 + layer.29.0 0.17467273 271.46661095 + layer.29.1 0.17545724 315.68939032 + layer.39.0 16.22751761 1711.89493792 + layer.39.1 26.19674268 1754.48742439 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 553.95655292 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9474324 +BPFP 0.7364 bits/point +EBPFP 0.7364 equivalent bits/point +MSE 553.956553 +---------------------- --------------------------------------------------------- +Time: 22.316s Load: 1.280s, Pack+Encode: 7.829s, Decode+Unpack: 13.207s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 553.9566 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001617-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.327s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,316B, BPFP=0.3845 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 634,216B, BPFP=0.3944 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,395,576B, BPFP=0.8678 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,417,232B, BPFP=0.8813 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,727,716B, BPFP=1.0743 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,759,724B, BPFP=1.0942 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,273,572B, BPFP=0.7919 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,339,824B, BPFP=0.8331 +⌛️ [2/4] FRONTEND: Frontend time: 7.669s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.896s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 20.85895838 + layer.9.1 0.14283950 16.46541706 + layer.19.0 0.09585176 157.37290075 + layer.19.1 0.13229247 62.36377746 + layer.29.0 0.10926771 45.60921582 + layer.29.1 0.10983113 60.65104167 + layer.39.0 13.84559555 1815.05969436 + layer.39.1 12.75833856 1975.78907991 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 519.27126068 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10166176 +BPFP 0.7902 bits/point +EBPFP 0.7902 equivalent bits/point +MSE 519.271261 +---------------------- --------------------------------------------------------- +Time: 21.892s Load: 1.327s, Pack+Encode: 7.669s, Decode+Unpack: 12.896s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 519.2713 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001630-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.299s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 710,740B, BPFP=0.4419 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 639,264B, BPFP=0.3975 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,447,472B, BPFP=0.9001 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,408,168B, BPFP=0.8756 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,632,948B, BPFP=1.0154 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,617,308B, BPFP=1.0057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,108,104B, BPFP=0.6890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,174,856B, BPFP=0.7305 +⌛️ [2/4] FRONTEND: Frontend time: 7.717s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.064s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 121.84912249 + layer.9.1 0.14345678 29.36538423 + layer.19.0 0.16166856 117.52147007 + layer.19.1 0.14880180 158.04114932 + layer.29.0 0.17070711 187.86942853 + layer.29.1 0.15868870 160.04743712 + layer.39.0 31.98565594 1826.33842725 + layer.39.1 38.57007372 1963.90369309 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 570.61701401 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9738860 +BPFP 0.7570 bits/point +EBPFP 0.7570 equivalent bits/point +MSE 570.617014 +---------------------- --------------------------------------------------------- +Time: 22.081s Load: 1.299s, Pack+Encode: 7.717s, Decode+Unpack: 13.064s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 570.6170 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001636-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.283s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 521,152B, BPFP=0.3241 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 525,688B, BPFP=0.3269 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,313,700B, BPFP=0.8169 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,301,140B, BPFP=0.8091 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,315,028B, BPFP=0.8177 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,331,972B, BPFP=0.8282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 910,092B, BPFP=0.5659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 940,936B, BPFP=0.5851 +⌛️ [2/4] FRONTEND: Frontend time: 7.421s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 4.45689445 + layer.9.1 0.03218400 4.57333328 + layer.19.0 0.03742503 19.59322394 + layer.19.1 0.04139693 10.75241265 + layer.29.0 0.11425402 66.15149932 + layer.29.1 0.11776626 109.10778812 + layer.39.0 23.31748448 1600.94794651 + layer.39.1 15.89369429 1499.77491245 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 414.41975134 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8159708 +BPFP 0.6342 bits/point +EBPFP 0.6342 equivalent bits/point +MSE 414.419751 +---------------------- --------------------------------------------------------- +Time: 21.749s Load: 1.283s, Pack+Encode: 7.421s, Decode+Unpack: 13.044s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 414.4198 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001639-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.281s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 604,760B, BPFP=0.3760 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 607,528B, BPFP=0.3778 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,360,688B, BPFP=0.8461 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,382,084B, BPFP=0.8594 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,393,376B, BPFP=0.8664 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,416,052B, BPFP=0.8805 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 812,160B, BPFP=0.5050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 810,288B, BPFP=0.5039 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.864s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 65.68470929 + layer.9.1 0.14315520 53.07243911 + layer.19.0 0.04114968 29.75296979 + layer.19.1 0.04120060 12.11359962 + layer.29.0 0.18627036 457.56498727 + layer.29.1 0.17990809 411.19914040 + layer.39.0 46.02158449 1646.55619230 + layer.39.1 44.38447151 1632.74578160 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 538.58622742 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8386936 +BPFP 0.6519 bits/point +EBPFP 0.6519 equivalent bits/point +MSE 538.586227 +---------------------- --------------------------------------------------------- +Time: 22.023s Load: 1.281s, Pack+Encode: 7.878s, Decode+Unpack: 12.864s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 538.5862 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001653-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.279s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 512,888B, BPFP=0.3189 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 516,976B, BPFP=0.3215 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,408,408B, BPFP=0.8758 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,382,636B, BPFP=0.8597 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,477,184B, BPFP=0.9185 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,489,824B, BPFP=0.9264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 954,940B, BPFP=0.5938 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 956,480B, BPFP=0.5948 +⌛️ [2/4] FRONTEND: Frontend time: 7.855s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.992s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64482133 4.35709356 + layer.9.1 0.03141260 8.32206353 + layer.19.0 3.18767318 6.28893441 + layer.19.1 3.18914595 6.34230366 + layer.29.0 4.14946039 27.19889167 + layer.29.1 4.13952905 32.36369787 + layer.39.0 7.50609877 1230.59344158 + layer.39.1 7.79272438 1240.98042025 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 319.55585582 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8699336 +BPFP 0.6762 bits/point +EBPFP 0.6762 equivalent bits/point +MSE 319.555856 +---------------------- --------------------------------------------------------- +Time: 22.125s Load: 1.279s, Pack+Encode: 7.855s, Decode+Unpack: 12.992s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 319.5559 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 581,948B, BPFP=0.3619 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 571,720B, BPFP=0.3555 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,357,680B, BPFP=0.8442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,381,308B, BPFP=0.8589 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,568,868B, BPFP=0.9755 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,567,544B, BPFP=0.9747 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 989,408B, BPFP=0.6152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 991,704B, BPFP=0.6167 +⌛️ [2/4] FRONTEND: Frontend time: 7.914s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.283s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 25.92089502 + layer.9.1 0.14140505 40.94767540 + layer.19.0 0.11753838 89.52373846 + layer.19.1 0.11213660 52.01370483 + layer.29.0 0.21817993 365.84793855 + layer.29.1 4.26279853 277.31389287 + layer.39.0 8.71778059 1397.72652022 + layer.39.1 8.43609532 1365.26376950 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 451.81976686 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9010180 +BPFP 0.7003 bits/point +EBPFP 0.7003 equivalent bits/point +MSE 451.819767 +---------------------- --------------------------------------------------------- +Time: 22.488s Load: 1.291s, Pack+Encode: 7.914s, Decode+Unpack: 13.283s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 451.8198 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001659-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.306s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 628,912B, BPFP=0.3911 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 656,396B, BPFP=0.4082 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,329,424B, BPFP=0.8267 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,344,420B, BPFP=0.8360 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,401,728B, BPFP=0.8716 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,518,308B, BPFP=0.9441 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,051,820B, BPFP=0.6540 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,110,800B, BPFP=0.6907 +⌛️ [2/4] FRONTEND: Frontend time: 7.883s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.221s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 24.86838636 + layer.9.1 0.11967093 41.12622373 + layer.19.0 0.14332279 25.18979077 + layer.19.1 0.14205440 34.11129268 + layer.29.0 0.15356100 48.17660578 + layer.29.1 0.14462723 47.82973177 + layer.39.0 8.04224558 1384.83158230 + layer.39.1 10.17930073 1482.28605540 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 386.05245860 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9041808 +BPFP 0.7028 bits/point +EBPFP 0.7028 equivalent bits/point +MSE 386.052459 +---------------------- --------------------------------------------------------- +Time: 22.410s Load: 1.306s, Pack+Encode: 7.883s, Decode+Unpack: 13.221s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 386.0525 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001671-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.299s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 520,000B, BPFP=0.3233 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 529,392B, BPFP=0.3292 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,411,412B, BPFP=0.8776 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,358,460B, BPFP=0.8447 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,260,376B, BPFP=0.7837 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,231,852B, BPFP=0.7660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 869,248B, BPFP=0.5405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 896,220B, BPFP=0.5573 +⌛️ [2/4] FRONTEND: Frontend time: 7.809s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.413s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 4.60990261 + layer.9.1 0.00091860 8.41850040 + layer.19.0 3.15620088 15.07363176 + layer.19.1 3.15238324 37.90094218 + layer.29.0 4.13387767 27.63642650 + layer.29.1 4.13737010 28.83148530 + layer.39.0 41.03603550 1393.21235275 + layer.39.1 41.15380502 1347.18560968 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 357.85860640 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8076960 +BPFP 0.6278 bits/point +EBPFP 0.6278 equivalent bits/point +MSE 357.858606 +---------------------- --------------------------------------------------------- +Time: 22.522s Load: 1.299s, Pack+Encode: 7.809s, Decode+Unpack: 13.413s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 357.8586 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001694-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 567,900B, BPFP=0.3531 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 578,184B, BPFP=0.3595 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,365,084B, BPFP=0.8488 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,394,400B, BPFP=0.8671 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,394,004B, BPFP=0.8668 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,487,972B, BPFP=0.9252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 941,140B, BPFP=0.5852 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 958,332B, BPFP=0.5959 +⌛️ [2/4] FRONTEND: Frontend time: 7.662s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.243s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 17.03451080 + layer.9.1 0.14279730 28.83754925 + layer.19.0 0.12708100 92.77055476 + layer.19.1 0.11978473 56.65284245 + layer.29.0 0.14591184 220.59109360 + layer.29.1 0.16402206 187.58508437 + layer.39.0 105.60261461 1689.18051576 + layer.39.1 191.64541547 2056.46609360 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 543.63978057 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8687016 +BPFP 0.6752 bits/point +EBPFP 0.6752 equivalent bits/point +MSE 543.639781 +---------------------- --------------------------------------------------------- +Time: 22.197s Load: 1.291s, Pack+Encode: 7.662s, Decode+Unpack: 13.243s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 543.6398 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001712-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.264s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 555,244B, BPFP=0.3453 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 538,668B, BPFP=0.3350 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,298,824B, BPFP=0.8076 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,289,696B, BPFP=0.8020 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,566,448B, BPFP=0.9740 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,508,008B, BPFP=0.9377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,018,160B, BPFP=0.6331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 968,456B, BPFP=0.6022 +⌛️ [2/4] FRONTEND: Frontend time: 7.762s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.358s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 4.31827947 + layer.9.1 0.14187527 4.31924515 + layer.19.0 0.05966252 56.40297178 + layer.19.1 0.05602499 52.06625080 + layer.29.0 0.10851584 38.54535279 + layer.29.1 0.10663395 68.58175143 + layer.39.0 36.66006795 2108.75230818 + layer.39.1 37.39855191 1801.15186246 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 516.76725276 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8743504 +BPFP 0.6796 bits/point +EBPFP 0.6796 equivalent bits/point +MSE 516.767253 +---------------------- --------------------------------------------------------- +Time: 22.384s Load: 1.264s, Pack+Encode: 7.762s, Decode+Unpack: 13.358s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 516.7673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001750-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.297s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 549,148B, BPFP=0.3415 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 540,508B, BPFP=0.3361 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,317,788B, BPFP=0.8194 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,322,000B, BPFP=0.8220 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,423,216B, BPFP=0.8850 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,473,600B, BPFP=0.9163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 856,108B, BPFP=0.5323 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 872,964B, BPFP=0.5428 +⌛️ [2/4] FRONTEND: Frontend time: 7.769s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.306s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 12.32733156 + layer.9.1 0.11247108 4.51067348 + layer.19.0 0.01001183 89.55202364 + layer.19.1 3.17262087 25.53737862 + layer.29.0 0.16690336 119.54682028 + layer.29.1 0.17317613 94.20381646 + layer.39.0 33.55914965 1739.17908309 + layer.39.1 10.63762287 1607.44508118 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 461.53777604 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8355332 +BPFP 0.6494 bits/point +EBPFP 0.6494 equivalent bits/point +MSE 461.537776 +---------------------- --------------------------------------------------------- +Time: 22.373s Load: 1.297s, Pack+Encode: 7.769s, Decode+Unpack: 13.306s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.5378 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.299s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 557,628B, BPFP=0.3467 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 599,900B, BPFP=0.3730 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,313,536B, BPFP=0.8168 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,358,288B, BPFP=0.8446 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,462,392B, BPFP=0.9093 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,585,288B, BPFP=0.9858 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 912,728B, BPFP=0.5675 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,110,184B, BPFP=0.6903 +⌛️ [2/4] FRONTEND: Frontend time: 7.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.110s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 4.31808733 + layer.9.1 0.03247940 4.35778222 + layer.19.0 0.20408508 189.11276266 + layer.19.1 0.20919449 279.97397326 + layer.29.0 0.13400092 171.82557307 + layer.29.1 0.12260655 230.66268704 + layer.39.0 13.98719058 1576.18863419 + layer.39.1 8.64389327 1576.86039478 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 504.16248682 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8899944 +BPFP 0.6918 bits/point +EBPFP 0.6918 equivalent bits/point +MSE 504.162487 +---------------------- --------------------------------------------------------- +Time: 21.958s Load: 1.299s, Pack+Encode: 7.549s, Decode+Unpack: 13.110s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 504.1625 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001838-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.312s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 591,488B, BPFP=0.3678 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 602,332B, BPFP=0.3745 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,347,184B, BPFP=0.8377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,356,664B, BPFP=0.8436 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,466,464B, BPFP=0.9119 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,469,176B, BPFP=0.9136 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,030,180B, BPFP=0.6406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,025,876B, BPFP=0.6379 +⌛️ [2/4] FRONTEND: Frontend time: 7.865s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.107s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 21.18123955 + layer.9.1 0.14463072 64.55229226 + layer.19.0 0.16931463 223.60040592 + layer.19.1 0.17979540 151.64418179 + layer.29.0 0.11737749 133.50074618 + layer.29.1 0.10948915 212.08241802 + layer.39.0 8.46774266 1393.85466412 + layer.39.1 8.48397517 1368.76472461 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 446.14758406 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8889364 +BPFP 0.6909 bits/point +EBPFP 0.6909 equivalent bits/point +MSE 446.147584 +---------------------- --------------------------------------------------------- +Time: 22.283s Load: 1.312s, Pack+Encode: 7.865s, Decode+Unpack: 13.107s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 446.1476 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001840-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.346s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 597,772B, BPFP=0.3717 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 601,708B, BPFP=0.3742 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,394,768B, BPFP=0.8673 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,391,592B, BPFP=0.8653 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,656,688B, BPFP=1.0302 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,650,772B, BPFP=1.0265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,138,608B, BPFP=0.7080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,114,408B, BPFP=0.6930 +⌛️ [2/4] FRONTEND: Frontend time: 7.902s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.135s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14223057 28.17918010 + layer.9.1 0.14268742 21.27323106 + layer.19.0 0.21739516 307.56659901 + layer.19.1 0.24972380 234.03042423 + layer.29.0 0.18828982 463.78963706 + layer.29.1 0.18108670 487.60291309 + layer.39.0 11.67542184 1840.44094238 + layer.39.1 15.11985385 1834.29894938 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 652.14773454 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9546316 +BPFP 0.7420 bits/point +EBPFP 0.7420 equivalent bits/point +MSE 652.147735 +---------------------- --------------------------------------------------------- +Time: 22.382s Load: 1.346s, Pack+Encode: 7.902s, Decode+Unpack: 13.135s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 652.1477 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001854-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.286s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 572,088B, BPFP=0.3557 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 580,852B, BPFP=0.3612 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,328,808B, BPFP=0.8263 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,346,060B, BPFP=0.8370 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,555,000B, BPFP=0.9669 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,548,608B, BPFP=0.9629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 996,680B, BPFP=0.6198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,002,668B, BPFP=0.6235 +⌛️ [2/4] FRONTEND: Frontend time: 7.737s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.443s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 4.59110137 + layer.9.1 0.14270393 8.31617991 + layer.19.0 0.11367196 74.83494011 + layer.19.1 0.12267420 56.95580627 + layer.29.0 0.13560262 116.36650350 + layer.29.1 0.14809222 211.33452722 + layer.39.0 10.32325245 1578.02292264 + layer.39.1 8.35688960 1527.76902260 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 447.27387545 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8930764 +BPFP 0.6942 bits/point +EBPFP 0.6942 equivalent bits/point +MSE 447.273875 +---------------------- --------------------------------------------------------- +Time: 22.467s Load: 1.286s, Pack+Encode: 7.737s, Decode+Unpack: 13.443s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 447.2739 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001855-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.284s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 519,152B, BPFP=0.3228 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 530,744B, BPFP=0.3300 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,246,804B, BPFP=0.7753 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,254,872B, BPFP=0.7803 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,286,636B, BPFP=0.8001 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,333,924B, BPFP=0.8295 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 933,696B, BPFP=0.5806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 920,124B, BPFP=0.5721 +⌛️ [2/4] FRONTEND: Frontend time: 7.692s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61171023 8.61704573 + layer.9.1 2.72679972 4.51508589 + layer.19.0 0.11263356 83.80897803 + layer.19.1 0.10212393 20.86906041 + layer.29.0 4.19513435 66.35496259 + layer.29.1 4.21594343 69.69567813 + layer.39.0 8.80532175 1420.96816301 + layer.39.1 9.27097449 1568.59057625 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 405.42744375 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8025952 +BPFP 0.6238 bits/point +EBPFP 0.6238 equivalent bits/point +MSE 405.427444 +---------------------- --------------------------------------------------------- +Time: 22.025s Load: 1.284s, Pack+Encode: 7.692s, Decode+Unpack: 13.049s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 405.4274 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001857-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.307s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 625,224B, BPFP=0.3888 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 620,632B, BPFP=0.3859 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,403,152B, BPFP=0.8725 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,415,816B, BPFP=0.8804 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,485,496B, BPFP=0.9237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,524,488B, BPFP=0.9480 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 964,832B, BPFP=0.5999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 952,628B, BPFP=0.5924 +⌛️ [2/4] FRONTEND: Frontend time: 7.706s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.141s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 174.03904011 + layer.9.1 0.14997165 69.63927690 + layer.19.0 0.15685862 139.65281757 + layer.19.1 0.13652294 75.58520873 + layer.29.0 0.22636045 216.29743712 + layer.29.1 0.21023706 343.98933461 + layer.39.0 31.35143565 1707.96975486 + layer.39.1 33.65704095 1852.66141356 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 572.47928543 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8992268 +BPFP 0.6989 bits/point +EBPFP 0.6989 equivalent bits/point +MSE 572.479285 +---------------------- --------------------------------------------------------- +Time: 22.153s Load: 1.307s, Pack+Encode: 7.706s, Decode+Unpack: 13.141s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.4793 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.305s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 565,748B, BPFP=0.3518 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 575,764B, BPFP=0.3580 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,356,356B, BPFP=0.8434 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,367,852B, BPFP=0.8506 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,514,108B, BPFP=0.9415 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,533,524B, BPFP=0.9536 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 995,904B, BPFP=0.6193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 978,260B, BPFP=0.6083 +⌛️ [2/4] FRONTEND: Frontend time: 7.752s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.407s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 4.31343366 + layer.9.1 0.14194651 12.81654056 + layer.19.0 0.13165920 56.28142411 + layer.19.1 0.11547583 115.50304441 + layer.29.0 4.19202371 109.70577642 + layer.29.1 0.11136677 129.80119787 + layer.39.0 9.51575185 1505.04902897 + layer.39.1 9.66679849 1510.78207577 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 430.53156522 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8887516 +BPFP 0.6908 bits/point +EBPFP 0.6908 equivalent bits/point +MSE 430.531565 +---------------------- --------------------------------------------------------- +Time: 22.464s Load: 1.305s, Pack+Encode: 7.752s, Decode+Unpack: 13.407s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 430.5316 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.280s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 487,704B, BPFP=0.3033 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 485,712B, BPFP=0.3020 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,218,404B, BPFP=0.7576 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,245,408B, BPFP=0.7744 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,049,452B, BPFP=0.6526 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,048,452B, BPFP=0.6519 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 795,864B, BPFP=0.4949 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 797,228B, BPFP=0.4957 +⌛️ [2/4] FRONTEND: Frontend time: 7.597s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.689s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.60361947 4.36716169 + layer.9.1 2.64162177 4.34624535 + layer.19.0 3.15421573 43.07485176 + layer.19.1 3.18597002 29.54818330 + layer.29.0 4.16148507 38.88464313 + layer.29.1 4.16879732 35.18611957 + layer.39.0 7.32495125 1103.48217128 + layer.39.1 7.16856507 1113.75549188 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 296.58060850 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7128224 +BPFP 0.5541 bits/point +EBPFP 0.5541 equivalent bits/point +MSE 296.580608 +---------------------- --------------------------------------------------------- +Time: 21.566s Load: 1.280s, Pack+Encode: 7.597s, Decode+Unpack: 12.689s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 296.5806 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.6714 bits/point +Avg EBPFP 0.6714 equivalent bits/point +Avg MSE 467.635080 +Avg Time 22.155s +------------------------ ---------------------------- diff --git a/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..235f42c1f42ce900f5df0881cd502232bc057638 --- /dev/null +++ b/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_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/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-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 hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.283s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 927,892B, BPFP=0.5770 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 971,200B, BPFP=0.6039 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,585,296B, BPFP=0.9858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,633,452B, BPFP=1.0157 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,596,104B, BPFP=0.9925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,670,136B, BPFP=1.0385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,006,132B, BPFP=0.6256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,031,120B, BPFP=0.6412 +⌛️ [2/4] FRONTEND: Frontend time: 7.889s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.335s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11100285 93.69900111 + layer.9.1 0.11103876 105.19355898 + layer.19.0 0.02553116 156.53884113 + layer.19.1 0.10833414 220.05511780 + layer.29.0 0.30844607 395.40500637 + layer.29.1 0.33610574 517.39095829 + layer.39.0 10.03071710 1759.04648201 + layer.39.1 10.11984639 1833.18003820 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 635.06362549 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10421332 +BPFP 0.8100 bits/point +EBPFP 0.8100 equivalent bits/point +MSE 635.063625 +---------------------- --------------------------------------------------------- +Time: 22.507s Load: 1.283s, Pack+Encode: 7.889s, Decode+Unpack: 13.335s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 635.0636 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000001-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.248s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 820,092B, BPFP=0.5099 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 856,336B, BPFP=0.5325 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,577,004B, BPFP=0.9806 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,635,940B, BPFP=1.0173 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,679,120B, BPFP=1.0441 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,758,256B, BPFP=1.0933 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,042,812B, BPFP=0.6484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,090,584B, BPFP=0.6781 +⌛️ [2/4] FRONTEND: Frontend time: 7.882s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.131s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61021196 12.73717939 + layer.9.1 2.61901253 12.32350117 + layer.19.0 3.15140481 52.47800263 + layer.19.1 3.16250889 66.32273261 + layer.29.0 4.15625404 136.85658429 + layer.29.1 4.15938147 46.60887257 + layer.39.0 10.95910936 1641.29703916 + layer.39.1 9.06533984 1662.66730341 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 453.91140190 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10460144 +BPFP 0.8130 bits/point +EBPFP 0.8130 equivalent bits/point +MSE 453.911402 +---------------------- --------------------------------------------------------- +Time: 22.261s Load: 1.248s, Pack+Encode: 7.882s, Decode+Unpack: 13.131s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 453.9114 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000045-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,119,068B, BPFP=0.6959 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,135,696B, BPFP=0.7062 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,821,664B, BPFP=1.1327 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,846,896B, BPFP=1.1484 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,989,732B, BPFP=1.2372 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,035,264B, BPFP=1.2656 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,244,416B, BPFP=0.7738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,280,596B, BPFP=0.7963 +⌛️ [2/4] FRONTEND: Frontend time: 7.904s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.307s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 138.62606455 + layer.9.1 0.14253284 165.21552650 + layer.19.0 0.09744245 249.20632362 + layer.19.1 0.13747554 276.61845352 + layer.29.0 4.19766265 137.53460283 + layer.29.1 4.20130152 161.75348217 + layer.39.0 38.53896798 2057.16109519 + layer.39.1 35.26563495 2099.73097740 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 660.73081572 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12473332 +BPFP 0.9695 bits/point +EBPFP 0.9695 equivalent bits/point +MSE 660.730816 +---------------------- --------------------------------------------------------- +Time: 22.447s Load: 1.236s, Pack+Encode: 7.904s, Decode+Unpack: 13.307s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 660.7308 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000064-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 946,508B, BPFP=0.5886 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 884,616B, BPFP=0.5501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,758,912B, BPFP=1.0937 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,721,764B, BPFP=1.0706 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,967,900B, BPFP=1.2237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,921,356B, BPFP=1.1947 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,270,280B, BPFP=0.7899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,278,796B, BPFP=0.7952 +⌛️ [2/4] FRONTEND: Frontend time: 7.860s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.275s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 37.54644968 + layer.9.1 0.03225276 8.82231972 + layer.19.0 0.11899935 157.53412528 + layer.19.1 0.11456829 116.24696554 + layer.29.0 0.13249551 220.47590337 + layer.29.1 0.12471250 224.51508278 + layer.39.0 10.78219516 2103.08277619 + layer.39.1 9.99374328 1885.91515441 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 594.26734712 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11750132 +BPFP 0.9133 bits/point +EBPFP 0.9133 equivalent bits/point +MSE 594.267347 +---------------------- --------------------------------------------------------- +Time: 22.379s Load: 1.244s, Pack+Encode: 7.860s, Decode+Unpack: 13.275s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 594.2673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000092-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 839,232B, BPFP=0.5218 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 843,208B, BPFP=0.5243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,647,240B, BPFP=1.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,629,516B, BPFP=1.0133 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,774,256B, BPFP=1.1033 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,738,392B, BPFP=1.0810 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,051,224B, BPFP=0.6537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,058,484B, BPFP=0.6582 +⌛️ [2/4] FRONTEND: Frontend time: 7.868s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.160s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 4.31649237 + layer.9.1 0.03227402 4.43936856 + layer.19.0 3.18865969 24.85486808 + layer.19.1 3.19251184 29.38346416 + layer.29.0 0.19572780 381.09781917 + layer.29.1 0.14992644 232.89207259 + layer.39.0 12.23891426 1924.60856415 + layer.39.1 9.64680585 1682.81470869 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 535.55091972 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10581552 +BPFP 0.8225 bits/point +EBPFP 0.8225 equivalent bits/point +MSE 535.550920 +---------------------- --------------------------------------------------------- +Time: 22.278s Load: 1.250s, Pack+Encode: 7.868s, Decode+Unpack: 13.160s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 535.5509 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000096-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.253s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 861,220B, BPFP=0.5355 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 881,664B, BPFP=0.5482 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,624,560B, BPFP=1.0102 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,660,336B, BPFP=1.0324 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,797,684B, BPFP=1.1178 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,841,612B, BPFP=1.1451 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,211,468B, BPFP=0.7533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,204,208B, BPFP=0.7488 +⌛️ [2/4] FRONTEND: Frontend time: 7.901s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.259s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 24.48295726 + layer.9.1 0.14248663 16.36841248 + layer.19.0 0.04071400 54.52322111 + layer.19.1 0.03715074 80.01178964 + layer.29.0 4.22673132 277.27821554 + layer.29.1 4.22861263 228.16927332 + layer.39.0 10.70292353 1762.40082776 + layer.39.1 9.44238934 1792.43998727 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 529.45933555 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11082752 +BPFP 0.8614 bits/point +EBPFP 0.8614 equivalent bits/point +MSE 529.459336 +---------------------- --------------------------------------------------------- +Time: 22.413s Load: 1.253s, Pack+Encode: 7.901s, Decode+Unpack: 13.259s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 529.4593 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000133-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,152,088B, BPFP=0.7164 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,157,636B, BPFP=0.7198 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,845,424B, BPFP=1.1475 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,842,676B, BPFP=1.1458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,982,252B, BPFP=1.2326 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,987,812B, BPFP=1.2361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,175,896B, BPFP=0.7312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,156,344B, BPFP=0.7190 +⌛️ [2/4] FRONTEND: Frontend time: 7.913s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.302s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 295.29707896 + layer.9.1 0.14203072 282.03991563 + layer.19.0 0.04969746 221.09479465 + layer.19.1 0.04852902 254.38421283 + layer.29.0 0.13952979 191.45254298 + layer.29.1 0.11857529 163.84780922 + layer.39.0 52.16041866 1943.34972939 + layer.39.1 64.85207736 1931.76074499 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 660.40335358 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12300128 +BPFP 0.9561 bits/point +EBPFP 0.9561 equivalent bits/point +MSE 660.403354 +---------------------- --------------------------------------------------------- +Time: 22.463s Load: 1.249s, Pack+Encode: 7.913s, Decode+Unpack: 13.302s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 660.4034 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000196-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 963,896B, BPFP=0.5994 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 958,840B, BPFP=0.5962 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,682,668B, BPFP=1.0463 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,705,384B, BPFP=1.0604 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,863,896B, BPFP=1.1590 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,888,748B, BPFP=1.1745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,197,744B, BPFP=0.7448 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,194,176B, BPFP=0.7426 +⌛️ [2/4] FRONTEND: Frontend time: 7.950s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.255s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 101.40695638 + layer.9.1 0.14255715 68.68603251 + layer.19.0 0.12077588 206.29684018 + layer.19.1 0.12364273 119.83858644 + layer.29.0 4.20710867 153.69400669 + layer.29.1 4.21108798 154.38459089 + layer.39.0 8.84959445 1799.10904171 + layer.39.1 9.12830806 1626.35418656 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 528.72128017 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11455352 +BPFP 0.8904 bits/point +EBPFP 0.8904 equivalent bits/point +MSE 528.721280 +---------------------- --------------------------------------------------------- +Time: 22.457s Load: 1.252s, Pack+Encode: 7.950s, Decode+Unpack: 13.255s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 528.7213 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000268-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,133,288B, BPFP=0.7047 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,151,708B, BPFP=0.7162 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,799,508B, BPFP=1.1190 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,818,452B, BPFP=1.1307 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,986,960B, BPFP=1.2355 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,999,948B, BPFP=1.2436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,338,184B, BPFP=0.8321 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,361,880B, BPFP=0.8468 +⌛️ [2/4] FRONTEND: Frontend time: 7.970s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.311s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 234.35108246 + layer.9.1 0.14262173 270.97755492 + layer.19.0 0.13202983 317.34395893 + layer.19.1 0.12978742 345.15011143 + layer.29.0 0.12169007 313.02734002 + layer.29.1 0.13371499 288.03271251 + layer.39.0 71.22791309 2210.75135307 + layer.39.1 35.82807525 2222.42566062 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 775.25747174 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12589928 +BPFP 0.9786 bits/point +EBPFP 0.9786 equivalent bits/point +MSE 775.257472 +---------------------- --------------------------------------------------------- +Time: 22.517s Load: 1.236s, Pack+Encode: 7.970s, Decode+Unpack: 13.311s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 775.2575 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000315-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 922,988B, BPFP=0.5739 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 922,356B, BPFP=0.5735 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,722,464B, BPFP=1.0711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,698,648B, BPFP=1.0562 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,928,872B, BPFP=1.1994 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,886,400B, BPFP=1.1730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,273,556B, BPFP=0.7919 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,218,844B, BPFP=0.7579 +⌛️ [2/4] FRONTEND: Frontend time: 7.838s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.320s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 28.73780991 + layer.9.1 0.14121198 37.01731137 + layer.19.0 0.08207523 170.93773878 + layer.19.1 0.11558007 162.65922477 + layer.29.0 0.16338114 353.87038364 + layer.29.1 0.15213004 285.90637934 + layer.39.0 27.31461666 2439.21760586 + layer.39.1 28.69002706 2169.98408150 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 706.04131689 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11574128 +BPFP 0.8996 bits/point +EBPFP 0.8996 equivalent bits/point +MSE 706.041317 +---------------------- --------------------------------------------------------- +Time: 22.399s Load: 1.240s, Pack+Encode: 7.838s, Decode+Unpack: 13.320s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 706.0413 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000322-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,044,632B, BPFP=0.6496 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,046,528B, BPFP=0.6507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,802,008B, BPFP=1.1205 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,798,796B, BPFP=1.1185 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,029,148B, BPFP=1.2618 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,026,348B, BPFP=1.2600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,357,364B, BPFP=0.8440 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,336,156B, BPFP=0.8308 +⌛️ [2/4] FRONTEND: Frontend time: 7.983s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.320s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 106.39076926 + layer.9.1 0.11112548 69.49189152 + layer.19.0 0.11343976 161.62460204 + layer.19.1 0.08227446 157.10588785 + layer.29.0 0.11178890 104.16520415 + layer.29.1 4.21559211 88.68603749 + layer.39.0 9.18455757 1821.07895575 + layer.39.1 8.88372284 1796.37185610 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 538.11440052 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12440980 +BPFP 0.9670 bits/point +EBPFP 0.9670 equivalent bits/point +MSE 538.114401 +---------------------- --------------------------------------------------------- +Time: 22.546s Load: 1.243s, Pack+Encode: 7.983s, Decode+Unpack: 13.320s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 538.1144 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000347-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,096,144B, BPFP=0.6816 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,059,112B, BPFP=0.6586 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,653,480B, BPFP=1.0282 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,650,500B, BPFP=1.0263 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,874,856B, BPFP=1.1658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,876,624B, BPFP=1.1669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,118,876B, BPFP=0.6957 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,126,892B, BPFP=0.7007 +⌛️ [2/4] FRONTEND: Frontend time: 7.973s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.274s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 542.75986947 + layer.9.1 0.14561824 467.20538841 + layer.19.0 0.12576092 313.89879815 + layer.19.1 0.12606844 215.55330707 + layer.29.0 0.19770402 188.18445559 + layer.29.1 0.18863435 193.03285180 + layer.39.0 84.70259273 2552.81948424 + layer.39.1 43.66404011 2437.41579115 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 863.85874323 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11456484 +BPFP 0.8905 bits/point +EBPFP 0.8905 equivalent bits/point +MSE 863.858743 +---------------------- --------------------------------------------------------- +Time: 22.488s Load: 1.241s, Pack+Encode: 7.973s, Decode+Unpack: 13.274s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 863.8587 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000352-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 867,076B, BPFP=0.5392 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 854,812B, BPFP=0.5315 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,505,676B, BPFP=0.9363 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,471,764B, BPFP=0.9152 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,484,768B, BPFP=0.9233 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,418,932B, BPFP=0.8823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 903,172B, BPFP=0.5616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 881,000B, BPFP=0.5478 +⌛️ [2/4] FRONTEND: Frontend time: 7.925s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 217.64903693 + layer.9.1 0.14295322 145.77557904 + layer.19.0 0.05949541 132.28061326 + layer.19.1 0.07012351 159.28719954 + layer.29.0 4.21949463 107.85796721 + layer.29.1 4.23773965 173.12104027 + layer.39.0 8.48589099 1467.73463865 + layer.39.1 10.46205428 1500.84559058 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 488.06895818 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9387200 +BPFP 0.7296 bits/point +EBPFP 0.7296 equivalent bits/point +MSE 488.068958 +---------------------- --------------------------------------------------------- +Time: 22.578s Load: 1.242s, Pack+Encode: 7.925s, Decode+Unpack: 13.411s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 488.0690 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000360-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 918,824B, BPFP=0.5713 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 885,100B, BPFP=0.5504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,565,752B, BPFP=0.9736 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,543,364B, BPFP=0.9597 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,450,888B, BPFP=0.9022 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,411,596B, BPFP=0.8778 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 800,588B, BPFP=0.4978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 782,616B, BPFP=0.4866 +⌛️ [2/4] FRONTEND: Frontend time: 7.798s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.215s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 85.51219755 + layer.9.1 0.00177230 53.29118911 + layer.19.0 0.01183476 76.15556849 + layer.19.1 0.01005667 81.84080010 + layer.29.0 4.18449569 44.60656936 + layer.29.1 4.18053255 64.35512178 + layer.39.0 7.97218927 1339.85848456 + layer.39.1 7.92115618 1352.62575613 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 387.28071089 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9358728 +BPFP 0.7274 bits/point +EBPFP 0.7274 equivalent bits/point +MSE 387.280711 +---------------------- --------------------------------------------------------- +Time: 22.253s Load: 1.241s, Pack+Encode: 7.798s, Decode+Unpack: 13.215s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 387.2807 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000389-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 913,544B, BPFP=0.5681 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 927,788B, BPFP=0.5769 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,663,884B, BPFP=1.0346 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,666,256B, BPFP=1.0361 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,765,464B, BPFP=1.0978 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,781,768B, BPFP=1.1079 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,150,108B, BPFP=0.7152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,153,496B, BPFP=0.7173 +⌛️ [2/4] FRONTEND: Frontend time: 7.895s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.294s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 28.85161722 + layer.9.1 0.03324844 20.96415851 + layer.19.0 0.13337831 212.74826886 + layer.19.1 0.12266011 179.44516078 + layer.29.0 4.22871927 279.13916746 + layer.29.1 4.21185188 166.31291786 + layer.39.0 10.68945623 1751.51257561 + layer.39.1 11.70080065 1614.85577841 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 531.72870559 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11022308 +BPFP 0.8567 bits/point +EBPFP 0.8567 equivalent bits/point +MSE 531.728706 +---------------------- --------------------------------------------------------- +Time: 22.430s Load: 1.241s, Pack+Encode: 7.895s, Decode+Unpack: 13.294s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 531.7287 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000429-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,015,360B, BPFP=0.6314 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,010,552B, BPFP=0.6284 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,694,304B, BPFP=1.0535 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,675,196B, BPFP=1.0417 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,801,672B, BPFP=1.1203 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,783,312B, BPFP=1.1089 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,076,688B, BPFP=0.6695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,080,032B, BPFP=0.6716 +⌛️ [2/4] FRONTEND: Frontend time: 7.950s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.324s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 303.30571474 + layer.9.1 0.14233285 331.29335801 + layer.19.0 0.14139387 309.30446116 + layer.19.1 0.13524239 262.58108485 + layer.29.0 0.16019033 245.59531200 + layer.29.1 0.14649145 192.82929401 + layer.39.0 12.41561455 1904.36660299 + layer.39.1 10.59172910 1823.27730022 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 671.56914100 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11137116 +BPFP 0.8657 bits/point +EBPFP 0.8657 equivalent bits/point +MSE 671.569141 +---------------------- --------------------------------------------------------- +Time: 22.519s Load: 1.245s, Pack+Encode: 7.950s, Decode+Unpack: 13.324s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 671.5691 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000436-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,034,760B, BPFP=0.6434 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,035,248B, BPFP=0.6437 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,741,112B, BPFP=1.0827 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,741,452B, BPFP=1.0829 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,876,044B, BPFP=1.1666 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,872,900B, BPFP=1.1646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,101,948B, BPFP=0.6852 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,107,820B, BPFP=0.6889 +⌛️ [2/4] FRONTEND: Frontend time: 7.954s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.298s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 69.47310271 + layer.9.1 0.03247534 73.57638391 + layer.19.0 0.03739121 70.63278017 + layer.19.1 0.03736199 39.45618434 + layer.29.0 4.17784350 70.57269779 + layer.29.1 4.17623735 71.99933341 + layer.39.0 10.57947434 1753.28971665 + layer.39.1 10.58388675 1719.63116842 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 483.57892092 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11511284 +BPFP 0.8947 bits/point +EBPFP 0.8947 equivalent bits/point +MSE 483.578921 +---------------------- --------------------------------------------------------- +Time: 22.493s Load: 1.241s, Pack+Encode: 7.954s, Decode+Unpack: 13.298s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 483.5789 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000442-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 866,652B, BPFP=0.5389 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 865,864B, BPFP=0.5384 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,601,592B, BPFP=0.9959 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,599,900B, BPFP=0.9948 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,742,052B, BPFP=1.0832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,729,476B, BPFP=1.0754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,097,436B, BPFP=0.6824 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,077,440B, BPFP=0.6700 +⌛️ [2/4] FRONTEND: Frontend time: 7.923s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.253s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 37.08226381 + layer.9.1 0.03247583 32.70742299 + layer.19.0 0.05000294 74.90078797 + layer.19.1 0.04728991 93.93916149 + layer.29.0 4.17616118 125.14532593 + layer.29.1 4.18555745 123.79376592 + layer.39.0 14.92630606 1505.83253741 + layer.39.1 15.22664209 1608.59471506 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 450.24949757 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10580412 +BPFP 0.8224 bits/point +EBPFP 0.8224 equivalent bits/point +MSE 450.249498 +---------------------- --------------------------------------------------------- +Time: 22.420s Load: 1.244s, Pack+Encode: 7.923s, Decode+Unpack: 13.253s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 450.2495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000447-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 952,580B, BPFP=0.5923 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 942,032B, BPFP=0.5858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,642,448B, BPFP=1.0213 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,614,712B, BPFP=1.0041 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,615,008B, BPFP=1.0042 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,590,660B, BPFP=0.9891 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 925,936B, BPFP=0.5758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 925,660B, BPFP=0.5756 +⌛️ [2/4] FRONTEND: Frontend time: 7.923s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.242s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 145.06148520 + layer.9.1 0.11516861 106.00914319 + layer.19.0 0.04822375 208.86298153 + layer.19.1 0.02465675 121.00937202 + layer.29.0 0.12445424 106.44061406 + layer.29.1 4.21809243 86.80246140 + layer.39.0 56.99443848 1741.13642152 + layer.39.1 29.63154648 1637.86310092 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 519.14819748 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10209036 +BPFP 0.7935 bits/point +EBPFP 0.7935 equivalent bits/point +MSE 519.148197 +---------------------- --------------------------------------------------------- +Time: 22.409s Load: 1.244s, Pack+Encode: 7.923s, Decode+Unpack: 13.242s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 519.1482 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000455-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 916,296B, BPFP=0.5698 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 974,852B, BPFP=0.6062 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,691,312B, BPFP=1.0517 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,755,860B, BPFP=1.0918 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,836,264B, BPFP=1.1418 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,940,056B, BPFP=1.2064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,197,720B, BPFP=0.7448 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,254,268B, BPFP=0.7799 +⌛️ [2/4] FRONTEND: Frontend time: 7.935s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.167s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 81.64057525 + layer.9.1 0.14323425 101.28880134 + layer.19.0 0.12097352 125.21716810 + layer.19.1 0.11863553 162.01327205 + layer.29.0 0.18810310 313.04823305 + layer.29.1 0.22084548 364.84081503 + layer.39.0 11.17468934 1730.94317096 + layer.39.1 12.52284677 1933.59789876 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 601.57374182 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11566628 +BPFP 0.8990 bits/point +EBPFP 0.8990 equivalent bits/point +MSE 601.573742 +---------------------- --------------------------------------------------------- +Time: 22.352s Load: 1.250s, Pack+Encode: 7.935s, Decode+Unpack: 13.167s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 601.5737 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000474-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 946,032B, BPFP=0.5883 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 942,144B, BPFP=0.5858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,708,188B, BPFP=1.0622 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,704,608B, BPFP=1.0600 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,880,032B, BPFP=1.1690 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,868,804B, BPFP=1.1621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,197,148B, BPFP=0.7444 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,190,636B, BPFP=0.7404 +⌛️ [2/4] FRONTEND: Frontend time: 7.917s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.333s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 49.21494448 + layer.9.1 0.14176414 93.44019620 + layer.19.0 0.11837582 84.90188236 + layer.19.1 0.11399856 97.48920527 + layer.29.0 0.14311602 223.46599411 + layer.29.1 0.14520382 228.27340019 + layer.39.0 14.59939236 1911.41706463 + layer.39.1 17.09091825 1953.45670169 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 580.20742362 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11437592 +BPFP 0.8890 bits/point +EBPFP 0.8890 equivalent bits/point +MSE 580.207424 +---------------------- --------------------------------------------------------- +Time: 22.495s Load: 1.245s, Pack+Encode: 7.917s, Decode+Unpack: 13.333s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 580.2074 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000476-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 879,784B, BPFP=0.5471 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 887,004B, BPFP=0.5516 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,619,328B, BPFP=1.0069 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,650,764B, BPFP=1.0265 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,787,820B, BPFP=1.1117 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,819,540B, BPFP=1.1314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,114,328B, BPFP=0.6929 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,180,960B, BPFP=0.7343 +⌛️ [2/4] FRONTEND: Frontend time: 7.959s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.329s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 21.01492857 + layer.9.1 0.14209374 29.08713139 + layer.19.0 0.05177973 62.23044512 + layer.19.1 0.05586525 102.32001154 + layer.29.0 0.12731753 151.20116006 + layer.29.1 0.12791453 123.17992876 + layer.39.0 10.91882437 1678.39143585 + layer.39.1 9.86751520 1533.75501433 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 462.64750695 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10939528 +BPFP 0.8503 bits/point +EBPFP 0.8503 equivalent bits/point +MSE 462.647507 +---------------------- --------------------------------------------------------- +Time: 22.529s Load: 1.241s, Pack+Encode: 7.959s, Decode+Unpack: 13.329s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 462.6475 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000479-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.237s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 834,828B, BPFP=0.5191 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 853,008B, BPFP=0.5304 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,520,576B, BPFP=0.9455 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,552,392B, BPFP=0.9653 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,727,440B, BPFP=1.0742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,794,320B, BPFP=1.1157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,158,480B, BPFP=0.7204 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,187,000B, BPFP=0.7381 +⌛️ [2/4] FRONTEND: Frontend time: 7.954s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.231s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 61.20304541 + layer.9.1 0.03257298 36.94223575 + layer.19.0 0.03929411 129.58855659 + layer.19.1 0.03736255 94.46095989 + layer.29.0 4.19976128 193.85345033 + layer.29.1 4.19887364 199.78748806 + layer.39.0 17.81771704 1885.17510347 + layer.39.1 13.24929237 1926.35705189 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 565.92098642 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10628044 +BPFP 0.8261 bits/point +EBPFP 0.8261 equivalent bits/point +MSE 565.920986 +---------------------- --------------------------------------------------------- +Time: 22.422s Load: 1.237s, Pack+Encode: 7.954s, Decode+Unpack: 13.231s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.9210 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000489-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,067,660B, BPFP=0.6639 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,098,296B, BPFP=0.6829 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,769,756B, BPFP=1.1005 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,794,140B, BPFP=1.1156 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,917,800B, BPFP=1.1925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,950,580B, BPFP=1.2129 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,211,660B, BPFP=0.7534 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,222,320B, BPFP=0.7601 +⌛️ [2/4] FRONTEND: Frontend time: 7.979s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.319s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 150.24464741 + layer.9.1 0.14206870 146.81363419 + layer.19.0 0.11541664 62.29708890 + layer.19.1 0.11639375 175.18350048 + layer.29.0 4.18928181 63.70252806 + layer.29.1 4.20210771 44.67487464 + layer.39.0 272.14109758 2442.42948106 + layer.39.1 217.56435053 2403.86755810 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 686.15166410 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12032212 +BPFP 0.9352 bits/point +EBPFP 0.9352 equivalent bits/point +MSE 686.151664 +---------------------- --------------------------------------------------------- +Time: 22.548s Load: 1.249s, Pack+Encode: 7.979s, Decode+Unpack: 13.319s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 686.1517 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,059,396B, BPFP=0.6587 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,063,108B, BPFP=0.6611 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,763,760B, BPFP=1.0967 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,780,436B, BPFP=1.1071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,985,200B, BPFP=1.2344 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,025,120B, BPFP=1.2593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,263,864B, BPFP=0.7859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,281,600B, BPFP=0.7969 +⌛️ [2/4] FRONTEND: Frontend time: 7.985s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.324s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 128.67440704 + layer.9.1 0.14265629 161.18884312 + layer.19.0 0.15235519 225.80024276 + layer.19.1 0.14002283 180.54268147 + layer.29.0 4.20702410 193.06890720 + layer.29.1 4.22502724 291.27150987 + layer.39.0 9.71896204 1864.23241006 + layer.39.1 14.02077861 1913.25596944 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 619.75437137 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12222484 +BPFP 0.9500 bits/point +EBPFP 0.9500 equivalent bits/point +MSE 619.754371 +---------------------- --------------------------------------------------------- +Time: 22.551s Load: 1.242s, Pack+Encode: 7.985s, Decode+Unpack: 13.324s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 619.7544 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000524-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 968,264B, BPFP=0.6021 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 942,012B, BPFP=0.5858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,738,920B, BPFP=1.0813 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,705,352B, BPFP=1.0604 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,983,020B, BPFP=1.2331 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,902,616B, BPFP=1.1831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,344,752B, BPFP=0.8362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,334,824B, BPFP=0.8300 +⌛️ [2/4] FRONTEND: Frontend time: 7.986s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.315s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 52.50329314 + layer.9.1 0.14327397 16.18949479 + layer.19.0 0.03872790 56.77843939 + layer.19.1 0.03991431 16.27390013 + layer.29.0 0.11363128 163.70040393 + layer.29.1 0.09618797 52.47845033 + layer.39.0 113.00349212 2655.76154091 + layer.39.1 66.70960681 2447.86373766 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 682.69365754 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11919760 +BPFP 0.9265 bits/point +EBPFP 0.9265 equivalent bits/point +MSE 682.693658 +---------------------- --------------------------------------------------------- +Time: 22.541s Load: 1.240s, Pack+Encode: 7.986s, Decode+Unpack: 13.315s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 682.6937 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000536-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,238,892B, BPFP=0.7704 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,202,056B, BPFP=0.7475 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,876,612B, BPFP=1.1669 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,859,624B, BPFP=1.1563 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,976,408B, BPFP=1.2290 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,939,812B, BPFP=1.2062 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,075,732B, BPFP=0.6689 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,078,856B, BPFP=0.6709 +⌛️ [2/4] FRONTEND: Frontend time: 8.004s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.373s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 246.48457896 + layer.9.1 0.14239137 154.87220431 + layer.19.0 0.03888746 76.32668239 + layer.19.1 0.04246985 106.70959686 + layer.29.0 0.10356636 109.04389526 + layer.29.1 0.10009016 85.23515600 + layer.39.0 8.56607607 2097.69245463 + layer.39.1 7.91790657 1967.32983126 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 605.46179996 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12247992 +BPFP 0.9520 bits/point +EBPFP 0.9520 equivalent bits/point +MSE 605.461800 +---------------------- --------------------------------------------------------- +Time: 22.620s Load: 1.243s, Pack+Encode: 8.004s, Decode+Unpack: 13.373s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 605.4618 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000546-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.248s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 948,164B, BPFP=0.5896 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,009,760B, BPFP=0.6279 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,586,828B, BPFP=0.9867 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,657,656B, BPFP=1.0308 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,603,076B, BPFP=0.9968 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,725,036B, BPFP=1.0727 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 974,816B, BPFP=0.6062 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 980,880B, BPFP=0.6099 +⌛️ [2/4] FRONTEND: Frontend time: 7.893s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.250s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 122.64175422 + layer.9.1 0.14243852 170.38011382 + layer.19.0 0.05701358 77.63858047 + layer.19.1 0.05730241 126.18885307 + layer.29.0 4.14713759 87.88522763 + layer.29.1 4.15440538 66.76635626 + layer.39.0 12.45677755 1823.91197071 + layer.39.1 14.71734096 1839.47866921 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 539.36144067 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10486216 +BPFP 0.8151 bits/point +EBPFP 0.8151 equivalent bits/point +MSE 539.361441 +---------------------- --------------------------------------------------------- +Time: 22.391s Load: 1.248s, Pack+Encode: 7.893s, Decode+Unpack: 13.250s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 539.3614 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000556-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.251s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,170,896B, BPFP=0.7281 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,151,872B, BPFP=0.7163 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,884,736B, BPFP=1.1720 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,869,928B, BPFP=1.1628 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,140,876B, BPFP=1.3312 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,103,192B, BPFP=1.3078 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,425,564B, BPFP=0.8864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,390,228B, BPFP=0.8645 +⌛️ [2/4] FRONTEND: Frontend time: 8.042s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.369s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 189.14792264 + layer.9.1 0.11180697 197.54530802 + layer.19.0 0.09949989 157.63023321 + layer.19.1 0.11883939 134.19007681 + layer.29.0 0.15177689 557.96056192 + layer.29.1 0.14123031 363.66002069 + layer.39.0 349.58010984 3210.97548551 + layer.39.1 334.73010188 2995.01177969 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 975.76517356 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 13137292 +BPFP 1.0211 bits/point +EBPFP 1.0211 equivalent bits/point +MSE 975.765174 +---------------------- --------------------------------------------------------- +Time: 22.662s Load: 1.251s, Pack+Encode: 8.042s, Decode+Unpack: 13.369s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 975.7652 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000620-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 807,196B, BPFP=0.5019 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 757,632B, BPFP=0.4711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,263,832B, BPFP=0.7859 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,147,672B, BPFP=0.7136 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,387,244B, BPFP=0.8626 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,260,276B, BPFP=0.7837 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 903,320B, BPFP=0.5617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 846,300B, BPFP=0.5262 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.378s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72630507 85.22323703 + layer.9.1 2.71889861 89.99200096 + layer.19.0 3.15508441 38.97055327 + layer.19.1 3.14332772 39.06631546 + layer.29.0 4.15805451 49.88167582 + layer.29.1 4.14588961 64.37447270 + layer.39.0 8.22539970 1344.90289717 + layer.39.1 8.64785859 1378.98901624 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 386.42502108 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8373472 +BPFP 0.6508 bits/point +EBPFP 0.6508 equivalent bits/point +MSE 386.425021 +---------------------- --------------------------------------------------------- +Time: 22.502s Load: 1.246s, Pack+Encode: 7.878s, Decode+Unpack: 13.378s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 386.4250 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000624-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 991,228B, BPFP=0.6164 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,065,684B, BPFP=0.6627 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,724,084B, BPFP=1.0721 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,816,708B, BPFP=1.1297 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,863,308B, BPFP=1.1586 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,977,368B, BPFP=1.2296 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,140,348B, BPFP=0.7091 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,221,768B, BPFP=0.7597 +⌛️ [2/4] FRONTEND: Frontend time: 7.923s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.266s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 49.23215138 + layer.9.1 0.11119189 90.22157553 + layer.19.0 0.08174444 71.00306431 + layer.19.1 0.08249469 57.89693768 + layer.29.0 4.18188438 347.99331423 + layer.29.1 4.20908200 206.23587233 + layer.39.0 9.33443395 1799.08309456 + layer.39.1 9.53268950 1827.07083731 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 556.09210592 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11800496 +BPFP 0.9172 bits/point +EBPFP 0.9172 equivalent bits/point +MSE 556.092106 +---------------------- --------------------------------------------------------- +Time: 22.439s Load: 1.250s, Pack+Encode: 7.923s, Decode+Unpack: 13.266s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 556.0921 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,055,296B, BPFP=0.6562 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,092,000B, BPFP=0.6790 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,707,368B, BPFP=1.0617 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,710,016B, BPFP=1.0633 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,722,004B, BPFP=1.0708 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,716,080B, BPFP=1.0671 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,000,648B, BPFP=0.6222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,011,108B, BPFP=0.6287 +⌛️ [2/4] FRONTEND: Frontend time: 7.928s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.194s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 207.02077364 + layer.9.1 0.03285184 276.46887934 + layer.19.0 0.04037820 103.13496896 + layer.19.1 0.04362713 66.53370742 + layer.29.0 0.11518513 90.59171044 + layer.29.1 0.11703357 67.52005731 + layer.39.0 256.78569723 1899.98615091 + layer.39.1 143.16752229 1835.03645336 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 568.28658767 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11014520 +BPFP 0.8561 bits/point +EBPFP 0.8561 equivalent bits/point +MSE 568.286588 +---------------------- --------------------------------------------------------- +Time: 22.367s Load: 1.245s, Pack+Encode: 7.928s, Decode+Unpack: 13.194s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 568.2866 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000676-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,103,704B, BPFP=0.6863 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,111,648B, BPFP=0.6912 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,720,764B, BPFP=1.0700 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,725,360B, BPFP=1.0729 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,762,432B, BPFP=1.0959 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,760,128B, BPFP=1.0945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,005,112B, BPFP=0.6250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 980,832B, BPFP=0.6099 +⌛️ [2/4] FRONTEND: Frontend time: 7.921s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.208s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 326.40727475 + layer.9.1 0.11256296 394.36250398 + layer.19.0 0.03396921 151.72914677 + layer.19.1 0.04105656 142.97190385 + layer.29.0 4.20373127 93.50737225 + layer.29.1 4.19418701 81.38118832 + layer.39.0 8.83613586 1559.00986947 + layer.39.1 8.48765384 1491.02929004 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 530.04981868 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11169980 +BPFP 0.8682 bits/point +EBPFP 0.8682 equivalent bits/point +MSE 530.049819 +---------------------- --------------------------------------------------------- +Time: 22.373s Load: 1.245s, Pack+Encode: 7.921s, Decode+Unpack: 13.208s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 530.0498 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000678-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,003,828B, BPFP=0.6242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 973,680B, BPFP=0.6055 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,763,740B, BPFP=1.0967 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,701,968B, BPFP=1.0583 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,918,236B, BPFP=1.1928 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,878,240B, BPFP=1.1679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,219,284B, BPFP=0.7582 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,173,640B, BPFP=0.7298 +⌛️ [2/4] FRONTEND: Frontend time: 8.014s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.160s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 73.76925143 + layer.9.1 0.03228644 93.56731535 + layer.19.0 0.12067159 99.10555954 + layer.19.1 0.11791951 98.54945678 + layer.29.0 0.15835167 337.13208373 + layer.29.1 0.15268422 371.93007800 + layer.39.0 158.29335801 2651.34145177 + layer.39.1 131.92238738 2711.98217128 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 804.67217098 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11632616 +BPFP 0.9042 bits/point +EBPFP 0.9042 equivalent bits/point +MSE 804.672171 +---------------------- --------------------------------------------------------- +Time: 22.426s Load: 1.252s, Pack+Encode: 8.014s, Decode+Unpack: 13.160s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 804.6722 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000684-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 931,596B, BPFP=0.5793 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 958,568B, BPFP=0.5961 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,679,232B, BPFP=1.0442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,702,108B, BPFP=1.0584 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,795,692B, BPFP=1.1166 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,851,020B, BPFP=1.1510 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,021,088B, BPFP=0.6349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,074,252B, BPFP=0.6680 +⌛️ [2/4] FRONTEND: Frontend time: 7.972s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.266s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 72.73922517 + layer.9.1 0.03230341 68.76313276 + layer.19.0 0.01113602 97.81451966 + layer.19.1 0.03747142 66.61333473 + layer.29.0 4.12172023 80.52443489 + layer.29.1 4.13913264 56.34111350 + layer.39.0 9.31610902 1564.19038523 + layer.39.1 11.00762596 1604.59726202 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 451.44792599 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11013556 +BPFP 0.8561 bits/point +EBPFP 0.8561 equivalent bits/point +MSE 451.447926 +---------------------- --------------------------------------------------------- +Time: 22.482s Load: 1.243s, Pack+Encode: 7.972s, Decode+Unpack: 13.266s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 451.4479 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000693-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.237s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 997,476B, BPFP=0.6202 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 978,948B, BPFP=0.6087 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,762,208B, BPFP=1.0958 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,741,948B, BPFP=1.0832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,928,364B, BPFP=1.1991 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,905,936B, BPFP=1.1851 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,154,524B, BPFP=0.7179 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,149,216B, BPFP=0.7146 +⌛️ [2/4] FRONTEND: Frontend time: 7.892s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.299s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14187056 100.76161055 + layer.9.1 0.14241365 52.93445559 + layer.19.0 0.11657135 212.68590815 + layer.19.1 0.11473399 184.21541706 + layer.29.0 0.16421308 248.73543457 + layer.29.1 0.18111406 389.71370583 + layer.39.0 55.30549089 2516.97612225 + layer.39.1 49.87731316 2548.97055078 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 781.87415060 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11618620 +BPFP 0.9031 bits/point +EBPFP 0.9031 equivalent bits/point +MSE 781.874151 +---------------------- --------------------------------------------------------- +Time: 22.428s Load: 1.237s, Pack+Encode: 7.892s, Decode+Unpack: 13.299s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 781.8742 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000713-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 882,568B, BPFP=0.5488 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 917,956B, BPFP=0.5708 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,592,976B, BPFP=0.9905 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,645,700B, BPFP=1.0233 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,733,584B, BPFP=1.0780 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,750,136B, BPFP=1.0883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,076,504B, BPFP=0.6694 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,035,204B, BPFP=0.6437 +⌛️ [2/4] FRONTEND: Frontend time: 7.897s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.205s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 12.78972411 + layer.9.1 0.03232725 24.27135317 + layer.19.0 0.03714494 24.79515381 + layer.19.1 0.03685654 15.72038040 + layer.29.0 4.16145554 50.40486708 + layer.29.1 4.17130075 45.10822091 + layer.39.0 7.63807493 1680.16698504 + layer.39.1 7.26751532 1417.49952245 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 408.84452587 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10634628 +BPFP 0.8266 bits/point +EBPFP 0.8266 equivalent bits/point +MSE 408.844526 +---------------------- --------------------------------------------------------- +Time: 22.351s Load: 1.250s, Pack+Encode: 7.897s, Decode+Unpack: 13.205s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 408.8445 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000734-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 916,616B, BPFP=0.5700 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 963,768B, BPFP=0.5993 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,566,016B, BPFP=0.9738 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,582,372B, BPFP=0.9839 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,710,348B, BPFP=1.0635 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,681,636B, BPFP=1.0457 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 983,296B, BPFP=0.6114 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,009,728B, BPFP=0.6279 +⌛️ [2/4] FRONTEND: Frontend time: 7.944s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.216s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 102.20834328 + layer.9.1 0.14394252 210.10056511 + layer.19.0 0.03713998 61.81214183 + layer.19.1 0.11359857 251.23742439 + layer.29.0 4.20669858 65.81156479 + layer.29.1 0.11083615 152.67547159 + layer.39.0 7.41086201 1352.14788284 + layer.39.1 8.74303628 1447.23623050 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 455.40370304 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10413780 +BPFP 0.8094 bits/point +EBPFP 0.8094 equivalent bits/point +MSE 455.403703 +---------------------- --------------------------------------------------------- +Time: 22.405s Load: 1.245s, Pack+Encode: 7.944s, Decode+Unpack: 13.216s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 455.4037 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000737-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,049,196B, BPFP=0.6524 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,104,744B, BPFP=0.6869 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,827,700B, BPFP=1.1365 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,861,996B, BPFP=1.1578 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,041,036B, BPFP=1.2691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,095,464B, BPFP=1.3030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,264,852B, BPFP=0.7865 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,313,884B, BPFP=0.8170 +⌛️ [2/4] FRONTEND: Frontend time: 7.957s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.212s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 100.89718641 + layer.9.1 0.14198353 81.33277121 + layer.19.0 0.17418623 185.08936246 + layer.19.1 0.18921874 279.75881487 + layer.29.0 0.15243895 222.85458453 + layer.29.1 0.17994503 271.02962830 + layer.39.0 13.57905399 1764.66666667 + layer.39.1 8.80701993 2039.56383317 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 618.14910595 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12558872 +BPFP 0.9762 bits/point +EBPFP 0.9762 equivalent bits/point +MSE 618.149106 +---------------------- --------------------------------------------------------- +Time: 22.411s Load: 1.242s, Pack+Encode: 7.957s, Decode+Unpack: 13.212s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 618.1491 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000804-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 990,000B, BPFP=0.6156 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 981,652B, BPFP=0.6104 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,767,796B, BPFP=1.0992 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,786,784B, BPFP=1.1111 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,997,328B, BPFP=1.2420 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,988,472B, BPFP=1.2365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,246,036B, BPFP=0.7748 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,198,428B, BPFP=0.7452 +⌛️ [2/4] FRONTEND: Frontend time: 7.993s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.374s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72336357 84.60086358 + layer.9.1 2.61637510 40.49849520 + layer.19.0 0.14860626 394.54851162 + layer.19.1 0.15499876 263.89991245 + layer.29.0 0.29089499 563.40850844 + layer.29.1 0.20993857 395.82672716 + layer.39.0 12.63850088 2212.30547596 + layer.39.1 9.97545753 1888.24355301 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 730.41650593 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11956496 +BPFP 0.9293 bits/point +EBPFP 0.9293 equivalent bits/point +MSE 730.416506 +---------------------- --------------------------------------------------------- +Time: 22.612s Load: 1.244s, Pack+Encode: 7.993s, Decode+Unpack: 13.374s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 730.4165 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000816-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,084,424B, BPFP=0.6743 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,119,128B, BPFP=0.6959 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,852,540B, BPFP=1.1519 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,870,208B, BPFP=1.1629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,132,800B, BPFP=1.3262 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,109,496B, BPFP=1.3117 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,316,056B, BPFP=0.8183 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,288,572B, BPFP=0.8013 +⌛️ [2/4] FRONTEND: Frontend time: 7.960s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.336s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 44.93150072 + layer.9.1 0.14187655 40.81687759 + layer.19.0 0.17405892 249.59732171 + layer.19.1 0.14315577 226.54695957 + layer.29.0 0.19218995 413.49526425 + layer.29.1 0.16272765 276.30004378 + layer.39.0 14.01399584 1974.50238777 + layer.39.1 9.48776763 1883.39637058 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 638.69834075 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12773224 +BPFP 0.9928 bits/point +EBPFP 0.9928 equivalent bits/point +MSE 638.698341 +---------------------- --------------------------------------------------------- +Time: 22.537s Load: 1.242s, Pack+Encode: 7.960s, Decode+Unpack: 13.336s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 638.6983 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000817-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,062,140B, BPFP=0.6605 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 964,788B, BPFP=0.5999 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,786,332B, BPFP=1.1108 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,705,184B, BPFP=1.0603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,907,464B, BPFP=1.1861 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,810,156B, BPFP=1.1256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,212,644B, BPFP=0.7540 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,165,688B, BPFP=0.7248 +⌛️ [2/4] FRONTEND: Frontend time: 7.921s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.257s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 166.55660021 + layer.9.1 0.14252999 69.53432923 + layer.19.0 0.12443910 212.60376870 + layer.19.1 0.13256963 185.08675581 + layer.29.0 4.20758094 62.83630313 + layer.29.1 4.18155761 61.03015063 + layer.39.0 45.67507362 2108.06478828 + layer.39.1 52.99942295 2012.28653295 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 609.74990362 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11614396 +BPFP 0.9028 bits/point +EBPFP 0.9028 equivalent bits/point +MSE 609.749904 +---------------------- --------------------------------------------------------- +Time: 22.421s Load: 1.243s, Pack+Encode: 7.921s, Decode+Unpack: 13.257s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 609.7499 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000880-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 933,444B, BPFP=0.5804 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 972,516B, BPFP=0.6047 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,690,476B, BPFP=1.0512 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,720,260B, BPFP=1.0697 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,809,304B, BPFP=1.1251 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,823,332B, BPFP=1.1338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,160,632B, BPFP=0.7217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,150,172B, BPFP=0.7152 +⌛️ [2/4] FRONTEND: Frontend time: 7.868s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.280s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 85.10168935 + layer.9.1 0.14194541 109.28747811 + layer.19.0 0.11782019 152.70942773 + layer.19.1 0.12099331 192.91049825 + layer.29.0 0.31534543 511.83826807 + layer.29.1 0.31351768 456.81805158 + layer.39.0 16.41217467 1891.48678765 + layer.39.1 11.15875965 1720.27889207 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 640.05388660 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11260136 +BPFP 0.8752 bits/point +EBPFP 0.8752 equivalent bits/point +MSE 640.053887 +---------------------- --------------------------------------------------------- +Time: 22.388s Load: 1.240s, Pack+Encode: 7.868s, Decode+Unpack: 13.280s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 640.0539 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 940,072B, BPFP=0.5846 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 904,160B, BPFP=0.5622 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,579,352B, BPFP=0.9821 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,475,940B, BPFP=0.9178 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,797,108B, BPFP=1.1175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,692,864B, BPFP=1.0527 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,130,056B, BPFP=0.7027 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,075,396B, BPFP=0.6687 +⌛️ [2/4] FRONTEND: Frontend time: 7.879s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.370s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 124.51715218 + layer.9.1 0.14279503 138.68293338 + layer.19.0 0.04409784 68.31208214 + layer.19.1 0.12204415 98.06801178 + layer.29.0 0.14332971 122.78555794 + layer.29.1 0.16018698 194.30684893 + layer.39.0 8.52841700 1732.61063356 + layer.39.1 19.04729908 1643.70885068 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 515.37400882 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10594948 +BPFP 0.8235 bits/point +EBPFP 0.8235 equivalent bits/point +MSE 515.374009 +---------------------- --------------------------------------------------------- +Time: 22.509s Load: 1.260s, Pack+Encode: 7.879s, Decode+Unpack: 13.370s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 515.3740 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000892-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,071,660B, BPFP=0.6664 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,052,096B, BPFP=0.6542 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,792,228B, BPFP=1.1144 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,772,676B, BPFP=1.1023 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,868,892B, BPFP=1.1621 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,838,420B, BPFP=1.1432 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,093,384B, BPFP=0.6799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,059,944B, BPFP=0.6591 +⌛️ [2/4] FRONTEND: Frontend time: 7.961s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.186s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 106.25290513 + layer.9.1 0.03263012 89.78004616 + layer.19.0 0.05225635 119.99179202 + layer.19.1 0.04916960 143.06043060 + layer.29.0 4.19413323 146.23128582 + layer.29.1 4.20728930 135.58743235 + layer.39.0 8.98594322 1751.94810570 + layer.39.1 8.30659896 1725.54680038 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 527.29984977 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11549300 +BPFP 0.8977 bits/point +EBPFP 0.8977 equivalent bits/point +MSE 527.299850 +---------------------- --------------------------------------------------------- +Time: 22.389s Load: 1.242s, Pack+Encode: 7.961s, Decode+Unpack: 13.186s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 527.2998 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000919-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.251s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 969,972B, BPFP=0.6031 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 946,408B, BPFP=0.5885 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,722,572B, BPFP=1.0711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,693,460B, BPFP=1.0530 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,871,716B, BPFP=1.1639 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,871,464B, BPFP=1.1637 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,192,324B, BPFP=0.7414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,185,392B, BPFP=0.7371 +⌛️ [2/4] FRONTEND: Frontend time: 7.987s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.321s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258133 77.85485315 + layer.9.1 0.03283905 94.02068410 + layer.19.0 0.03703246 56.46269600 + layer.19.1 0.03684524 43.18164995 + layer.29.0 0.11326863 118.07505571 + layer.29.1 0.10834243 84.04663125 + layer.39.0 11.60468402 2024.24466730 + layer.39.1 14.87000682 1857.05189430 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 544.36726647 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11453308 +BPFP 0.8902 bits/point +EBPFP 0.8902 equivalent bits/point +MSE 544.367266 +---------------------- --------------------------------------------------------- +Time: 22.559s Load: 1.251s, Pack+Encode: 7.987s, Decode+Unpack: 13.321s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 544.3673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000925-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 984,180B, BPFP=0.6120 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 996,336B, BPFP=0.6195 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,733,092B, BPFP=1.0777 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,733,460B, BPFP=1.0779 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,937,508B, BPFP=1.2048 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,925,944B, BPFP=1.1976 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,215,804B, BPFP=0.7560 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,198,168B, BPFP=0.7450 +⌛️ [2/4] FRONTEND: Frontend time: 7.799s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.243s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 49.45855719 + layer.9.1 0.11188250 53.05278972 + layer.19.0 3.25906142 247.67796880 + layer.19.1 3.26015426 179.88200414 + layer.29.0 4.19564952 366.24844795 + layer.29.1 4.21244012 343.06562401 + layer.39.0 303.99934336 2892.78446355 + layer.39.1 331.94728988 2616.05698822 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 843.52835545 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11724492 +BPFP 0.9113 bits/point +EBPFP 0.9113 equivalent bits/point +MSE 843.528355 +---------------------- --------------------------------------------------------- +Time: 22.287s Load: 1.245s, Pack+Encode: 7.799s, Decode+Unpack: 13.243s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 843.5284 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000927-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 901,228B, BPFP=0.5604 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 882,664B, BPFP=0.5489 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,688,532B, BPFP=1.0500 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,668,720B, BPFP=1.0376 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,749,820B, BPFP=1.0881 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,731,876B, BPFP=1.0769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,037,760B, BPFP=0.6453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 987,060B, BPFP=0.6138 +⌛️ [2/4] FRONTEND: Frontend time: 7.917s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.303s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 16.59037229 + layer.9.1 0.00271392 8.54160635 + layer.19.0 3.19073251 56.29686505 + layer.19.1 3.15044721 28.86788642 + layer.29.0 4.17151372 30.73356913 + layer.29.1 4.17302847 30.70820400 + layer.39.0 85.12206503 2585.01082458 + layer.39.1 85.43754975 2557.57115568 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 664.29006044 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10647660 +BPFP 0.8276 bits/point +EBPFP 0.8276 equivalent bits/point +MSE 664.290060 +---------------------- --------------------------------------------------------- +Time: 22.467s Load: 1.247s, Pack+Encode: 7.917s, Decode+Unpack: 13.303s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 664.2901 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000942-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 986,912B, BPFP=0.6137 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 948,544B, BPFP=0.5898 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,717,916B, BPFP=1.0682 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,656,876B, BPFP=1.0303 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,909,108B, BPFP=1.1871 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,803,832B, BPFP=1.1217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,280,064B, BPFP=0.7960 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,220,540B, BPFP=0.7590 +⌛️ [2/4] FRONTEND: Frontend time: 7.944s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.316s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 44.82226998 + layer.9.1 2.75948239 93.02274355 + layer.19.0 0.15224024 266.00147246 + layer.19.1 0.13045117 270.28147883 + layer.29.0 0.13097460 397.93720153 + layer.29.1 0.13177276 282.34487424 + layer.39.0 10.49186664 1950.43441579 + layer.39.1 12.55703299 1869.48981216 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 646.79178357 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11523792 +BPFP 0.8957 bits/point +EBPFP 0.8957 equivalent bits/point +MSE 646.791784 +---------------------- --------------------------------------------------------- +Time: 22.503s Load: 1.243s, Pack+Encode: 7.944s, Decode+Unpack: 13.316s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 646.7918 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000946-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 872,680B, BPFP=0.5426 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 896,916B, BPFP=0.5577 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,548,760B, BPFP=0.9630 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,579,960B, BPFP=0.9824 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,638,396B, BPFP=1.0188 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,680,228B, BPFP=1.0448 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,044,444B, BPFP=0.6495 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,062,512B, BPFP=0.6607 +⌛️ [2/4] FRONTEND: Frontend time: 7.841s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.186s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 29.15963268 + layer.9.1 0.03228249 57.59713268 + layer.19.0 0.04154089 132.38867001 + layer.19.1 0.04120101 144.98610116 + layer.29.0 4.21417063 127.22795288 + layer.29.1 4.21428318 126.22916667 + layer.39.0 28.58093312 1941.09869468 + layer.39.1 17.10356972 1908.79465138 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 558.43525027 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10323896 +BPFP 0.8024 bits/point +EBPFP 0.8024 equivalent bits/point +MSE 558.435250 +---------------------- --------------------------------------------------------- +Time: 22.273s Load: 1.245s, Pack+Encode: 7.841s, Decode+Unpack: 13.186s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 558.4353 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000959-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.248s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,043,352B, BPFP=0.6488 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,037,396B, BPFP=0.6451 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,791,544B, BPFP=1.1140 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,794,576B, BPFP=1.1159 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,967,132B, BPFP=1.2232 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,970,920B, BPFP=1.2256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,273,432B, BPFP=0.7918 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,275,028B, BPFP=0.7928 +⌛️ [2/4] FRONTEND: Frontend time: 7.905s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.344s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 149.26170010 + layer.9.1 0.14242138 136.63397405 + layer.19.0 0.13512425 275.57991086 + layer.19.1 0.13152432 302.71979465 + layer.29.0 0.11439834 220.36521012 + layer.29.1 0.11806111 193.66344317 + layer.39.0 18.41482236 2025.13721745 + layer.39.1 20.38586935 2044.56208214 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 668.49041657 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12153380 +BPFP 0.9446 bits/point +EBPFP 0.9446 equivalent bits/point +MSE 668.490417 +---------------------- --------------------------------------------------------- +Time: 22.498s Load: 1.248s, Pack+Encode: 7.905s, Decode+Unpack: 13.344s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 668.4904 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000972-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 866,064B, BPFP=0.5385 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 855,392B, BPFP=0.5319 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,564,640B, BPFP=0.9729 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,546,948B, BPFP=0.9619 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,707,064B, BPFP=1.0615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,669,380B, BPFP=1.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,166,936B, BPFP=0.7256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,089,728B, BPFP=0.6776 +⌛️ [2/4] FRONTEND: Frontend time: 7.852s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.146s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 88.41827841 + layer.9.1 0.14251336 57.73853868 + layer.19.0 0.11881898 136.40011143 + layer.19.1 0.11371834 86.71109917 + layer.29.0 0.15377442 171.50039796 + layer.29.1 0.16319071 150.78966691 + layer.39.0 9.10150218 1632.25501433 + layer.39.1 9.15265777 1704.91658707 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 503.59121175 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10466152 +BPFP 0.8135 bits/point +EBPFP 0.8135 equivalent bits/point +MSE 503.591212 +---------------------- --------------------------------------------------------- +Time: 22.237s Load: 1.239s, Pack+Encode: 7.852s, Decode+Unpack: 13.146s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 503.5912 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001000-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.238s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 836,888B, BPFP=0.5204 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 841,204B, BPFP=0.5231 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,494,596B, BPFP=0.9294 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,506,756B, BPFP=0.9369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,485,800B, BPFP=0.9239 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,488,284B, BPFP=0.9254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,095,644B, BPFP=0.6813 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,072,224B, BPFP=0.6667 +⌛️ [2/4] FRONTEND: Frontend time: 7.845s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.245s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 64.41971108 + layer.9.1 0.14223260 33.10605201 + layer.19.0 0.05715554 201.18700255 + layer.19.1 0.06015340 253.81383317 + layer.29.0 0.19165729 353.08026902 + layer.29.1 0.21090307 292.43336119 + layer.39.0 19.07211701 1681.73193251 + layer.39.1 16.66110887 1713.55523719 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 574.16592484 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9821396 +BPFP 0.7634 bits/point +EBPFP 0.7634 equivalent bits/point +MSE 574.165925 +---------------------- --------------------------------------------------------- +Time: 22.328s Load: 1.238s, Pack+Encode: 7.845s, Decode+Unpack: 13.245s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 574.1659 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,014,252B, BPFP=0.6307 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 981,400B, BPFP=0.6103 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,799,180B, BPFP=1.1188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,769,720B, BPFP=1.1004 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,013,700B, BPFP=1.2522 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,967,964B, BPFP=1.2237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,313,572B, BPFP=0.8168 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,244,128B, BPFP=0.7736 +⌛️ [2/4] FRONTEND: Frontend time: 7.970s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.282s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 64.86721486 + layer.9.1 0.14288678 61.73196235 + layer.19.0 0.11144568 134.74733365 + layer.19.1 0.11742487 170.13823225 + layer.29.0 0.11418290 164.06894699 + layer.29.1 0.10734091 114.66744269 + layer.39.0 54.48020137 2358.39000318 + layer.39.1 66.40954314 2483.98217128 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 694.07416341 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12103916 +BPFP 0.9408 bits/point +EBPFP 0.9408 equivalent bits/point +MSE 694.074163 +---------------------- --------------------------------------------------------- +Time: 22.494s Load: 1.241s, Pack+Encode: 7.970s, Decode+Unpack: 13.282s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 694.0742 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001056-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 899,004B, BPFP=0.5590 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 882,456B, BPFP=0.5487 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,650,804B, BPFP=1.0265 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,621,076B, BPFP=1.0080 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,781,592B, BPFP=1.1078 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,726,916B, BPFP=1.0738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,163,284B, BPFP=0.7233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,125,548B, BPFP=0.6999 +⌛️ [2/4] FRONTEND: Frontend time: 7.937s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.209s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 24.22938803 + layer.9.1 0.00081411 12.35492031 + layer.19.0 0.01015774 70.94539458 + layer.19.1 3.16362350 39.83036851 + layer.29.0 4.19769406 81.97975366 + layer.29.1 4.18061463 66.66344317 + layer.39.0 8.41366640 1772.53454314 + layer.39.1 8.38033145 1800.35100287 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 483.61110178 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10850680 +BPFP 0.8434 bits/point +EBPFP 0.8434 equivalent bits/point +MSE 483.611102 +---------------------- --------------------------------------------------------- +Time: 22.390s Load: 1.243s, Pack+Encode: 7.937s, Decode+Unpack: 13.209s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 483.6111 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001074-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 812,356B, BPFP=0.5051 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 834,372B, BPFP=0.5188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,647,256B, BPFP=1.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,652,112B, BPFP=1.0273 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,784,508B, BPFP=1.1096 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,815,480B, BPFP=1.1289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,168,148B, BPFP=0.7264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,160,848B, BPFP=0.7218 +⌛️ [2/4] FRONTEND: Frontend time: 7.928s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.236s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 12.58497617 + layer.9.1 0.03271215 4.45301805 + layer.19.0 3.19210144 61.62850207 + layer.19.1 3.19171965 24.70444574 + layer.29.0 0.11530653 123.34043696 + layer.29.1 0.10966549 109.08944206 + layer.39.0 16.12381606 1977.02101242 + layer.39.1 25.33235335 2224.42613817 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 567.15599645 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10875080 +BPFP 0.8453 bits/point +EBPFP 0.8453 equivalent bits/point +MSE 567.155996 +---------------------- --------------------------------------------------------- +Time: 22.414s Load: 1.250s, Pack+Encode: 7.928s, Decode+Unpack: 13.236s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 567.1560 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001078-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 800,084B, BPFP=0.4975 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 839,716B, BPFP=0.5221 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,591,700B, BPFP=0.9897 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,670,172B, BPFP=1.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,695,272B, BPFP=1.0541 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,864,356B, BPFP=1.1593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,179,772B, BPFP=0.7336 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,236,752B, BPFP=0.7690 +⌛️ [2/4] FRONTEND: Frontend time: 7.898s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.299s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64207787 4.32594460 + layer.9.1 0.03100527 4.45328481 + layer.19.0 3.19321449 29.65672755 + layer.19.1 3.20089330 34.47514725 + layer.29.0 0.10652387 163.58123408 + layer.29.1 0.17364564 566.93545049 + layer.39.0 9.89558772 1695.71728749 + layer.39.1 12.87769495 2091.12846227 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 573.78419232 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10877824 +BPFP 0.8455 bits/point +EBPFP 0.8455 equivalent bits/point +MSE 573.784192 +---------------------- --------------------------------------------------------- +Time: 22.442s Load: 1.244s, Pack+Encode: 7.898s, Decode+Unpack: 13.299s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 573.7842 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001086-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 828,704B, BPFP=0.5153 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 843,364B, BPFP=0.5244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,671,696B, BPFP=1.0395 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,638,440B, BPFP=1.0188 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,837,444B, BPFP=1.1426 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,857,880B, BPFP=1.1553 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,195,708B, BPFP=0.7435 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,208,520B, BPFP=0.7515 +⌛️ [2/4] FRONTEND: Frontend time: 7.917s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.284s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 16.41896241 + layer.9.1 0.03183258 16.32809142 + layer.19.0 0.03873757 52.33015461 + layer.19.1 0.03841183 70.77883238 + layer.29.0 0.10242378 90.20313992 + layer.29.1 0.10979955 306.21378542 + layer.39.0 11.55027136 2202.28844317 + layer.39.1 12.74680635 2536.98073862 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 661.44276849 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11081756 +BPFP 0.8614 bits/point +EBPFP 0.8614 equivalent bits/point +MSE 661.442768 +---------------------- --------------------------------------------------------- +Time: 22.441s Load: 1.239s, Pack+Encode: 7.917s, Decode+Unpack: 13.284s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 661.4428 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001102-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,045,872B, BPFP=0.6503 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,042,960B, BPFP=0.6485 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,858,092B, BPFP=1.1554 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,858,176B, BPFP=1.1554 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,035,320B, BPFP=1.2656 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,053,112B, BPFP=1.2767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,319,736B, BPFP=0.8206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,328,872B, BPFP=0.8263 +⌛️ [2/4] FRONTEND: Frontend time: 8.017s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.360s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 29.03177481 + layer.9.1 0.03112686 12.76817072 + layer.19.0 0.03695946 60.62528852 + layer.19.1 0.03932408 56.08054760 + layer.29.0 0.11080087 174.23151465 + layer.29.1 0.12351766 212.02027619 + layer.39.0 27.63217079 2346.30579433 + layer.39.1 35.42625259 2380.61731933 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 658.96008577 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12542140 +BPFP 0.9749 bits/point +EBPFP 0.9749 equivalent bits/point +MSE 658.960086 +---------------------- --------------------------------------------------------- +Time: 22.616s Load: 1.239s, Pack+Encode: 8.017s, Decode+Unpack: 13.360s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 658.9601 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001107-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.253s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 948,012B, BPFP=0.5895 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 951,984B, BPFP=0.5920 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,703,852B, BPFP=1.0595 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,718,084B, BPFP=1.0683 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,906,928B, BPFP=1.1858 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,903,308B, BPFP=1.1835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,228,960B, BPFP=0.7642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,197,432B, BPFP=0.7446 +⌛️ [2/4] FRONTEND: Frontend time: 7.914s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.344s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 32.71850127 + layer.9.1 0.11126176 41.35618633 + layer.19.0 0.00622823 70.07325493 + layer.19.1 0.00986777 15.66031295 + layer.29.0 4.20227933 123.79467128 + layer.29.1 4.19170939 146.97962432 + layer.39.0 64.89367936 1980.39509710 + layer.39.1 48.85537050 2116.10538045 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 565.88537858 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11558560 +BPFP 0.8984 bits/point +EBPFP 0.8984 equivalent bits/point +MSE 565.885379 +---------------------- --------------------------------------------------------- +Time: 22.511s Load: 1.253s, Pack+Encode: 7.914s, Decode+Unpack: 13.344s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.8854 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001116-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 947,376B, BPFP=0.5891 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 954,356B, BPFP=0.5934 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,701,588B, BPFP=1.0581 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,719,680B, BPFP=1.0693 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,848,364B, BPFP=1.1493 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,868,092B, BPFP=1.1616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,135,008B, BPFP=0.7058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,151,172B, BPFP=0.7158 +⌛️ [2/4] FRONTEND: Frontend time: 7.941s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.254s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 8.38584385 + layer.9.1 0.03110840 16.56889725 + layer.19.0 0.11193399 84.66943251 + layer.19.1 0.11167925 116.18114255 + layer.29.0 0.13638519 392.17363101 + layer.29.1 0.13233996 279.18035657 + layer.39.0 10.36537055 1975.71474053 + layer.39.1 10.25938570 1877.36262337 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 593.77958345 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11325636 +BPFP 0.8803 bits/point +EBPFP 0.8803 equivalent bits/point +MSE 593.779583 +---------------------- --------------------------------------------------------- +Time: 22.436s Load: 1.241s, Pack+Encode: 7.941s, Decode+Unpack: 13.254s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 593.7796 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001125-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 940,972B, BPFP=0.5851 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 969,408B, BPFP=0.6028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,682,276B, BPFP=1.0461 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,711,172B, BPFP=1.0640 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,823,304B, BPFP=1.1338 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,860,304B, BPFP=1.1568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,160,396B, BPFP=0.7216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,202,768B, BPFP=0.7479 +⌛️ [2/4] FRONTEND: Frontend time: 7.860s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.240s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14239891 32.75556152 + layer.9.1 0.14185137 24.72238638 + layer.19.0 0.03937967 103.12991484 + layer.19.1 0.04081462 56.38100923 + layer.29.0 4.18784542 133.27470352 + layer.29.1 4.19318340 104.38420288 + layer.39.0 9.46241929 1737.78605540 + layer.39.1 9.25020271 1771.97580388 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 495.55120471 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11350600 +BPFP 0.8822 bits/point +EBPFP 0.8822 equivalent bits/point +MSE 495.551205 +---------------------- --------------------------------------------------------- +Time: 22.342s Load: 1.242s, Pack+Encode: 7.860s, Decode+Unpack: 13.240s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 495.5512 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001139-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 985,156B, BPFP=0.6126 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 982,904B, BPFP=0.6112 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,690,564B, BPFP=1.0512 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,691,400B, BPFP=1.0517 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,795,396B, BPFP=1.1164 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,821,352B, BPFP=1.1325 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,115,656B, BPFP=0.6937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,103,276B, BPFP=0.6860 +⌛️ [2/4] FRONTEND: Frontend time: 7.992s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.313s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 57.87687042 + layer.9.1 0.14180939 56.74167761 + layer.19.0 0.04123239 116.44468322 + layer.19.1 0.03889530 107.90820002 + layer.29.0 0.17016378 201.84161095 + layer.29.1 0.15026704 230.18294333 + layer.39.0 12.11620503 1921.06797198 + layer.39.1 10.53236554 1843.66220949 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 566.96577088 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11185704 +BPFP 0.8694 bits/point +EBPFP 0.8694 equivalent bits/point +MSE 566.965771 +---------------------- --------------------------------------------------------- +Time: 22.550s Load: 1.246s, Pack+Encode: 7.992s, Decode+Unpack: 13.313s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 566.9658 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001145-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,004,072B, BPFP=0.6243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 967,128B, BPFP=0.6014 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,741,456B, BPFP=1.0829 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,688,360B, BPFP=1.0498 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,912,920B, BPFP=1.1895 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,854,144B, BPFP=1.1529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,196,832B, BPFP=0.7442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,204,024B, BPFP=0.7487 +⌛️ [2/4] FRONTEND: Frontend time: 7.832s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.282s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 85.48855858 + layer.9.1 0.11141965 62.11172795 + layer.19.0 0.02960617 126.32123528 + layer.19.1 0.09893673 75.88650111 + layer.29.0 0.11288278 165.97529648 + layer.29.1 0.12156463 117.14131646 + layer.39.0 13.31952528 2108.64183381 + layer.39.1 8.92088009 1847.94126074 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 573.68846630 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11568936 +BPFP 0.8992 bits/point +EBPFP 0.8992 equivalent bits/point +MSE 573.688466 +---------------------- --------------------------------------------------------- +Time: 22.359s Load: 1.244s, Pack+Encode: 7.832s, Decode+Unpack: 13.282s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 573.6885 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001171-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 915,200B, BPFP=0.5691 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 933,928B, BPFP=0.5807 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,724,652B, BPFP=1.0724 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,761,552B, BPFP=1.0954 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,929,308B, BPFP=1.1997 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,962,876B, BPFP=1.2205 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,213,656B, BPFP=0.7547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,181,148B, BPFP=0.7345 +⌛️ [2/4] FRONTEND: Frontend time: 7.941s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.248s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 20.40310983 + layer.9.1 0.03269095 32.72127706 + layer.19.0 0.03939078 149.15317574 + layer.19.1 0.03751187 92.91199061 + layer.29.0 0.14354374 339.95244349 + layer.29.1 0.12315212 173.19145973 + layer.39.0 10.67588198 1971.80579433 + layer.39.1 12.04857131 1782.81200255 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 570.36890667 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11622320 +BPFP 0.9034 bits/point +EBPFP 0.9034 equivalent bits/point +MSE 570.368907 +---------------------- --------------------------------------------------------- +Time: 22.440s Load: 1.250s, Pack+Encode: 7.941s, Decode+Unpack: 13.248s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 570.3689 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001179-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 927,924B, BPFP=0.5770 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 915,864B, BPFP=0.5695 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,731,828B, BPFP=1.0769 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,721,272B, BPFP=1.0703 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,885,900B, BPFP=1.1727 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,868,916B, BPFP=1.1621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,184,796B, BPFP=0.7367 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,189,488B, BPFP=0.7396 +⌛️ [2/4] FRONTEND: Frontend time: 7.932s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.295s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 28.67021102 + layer.9.1 0.03246013 20.72110171 + layer.19.0 0.05054442 239.35709169 + layer.19.1 0.04990058 175.03736867 + layer.29.0 4.26185866 298.82330468 + layer.29.1 4.26378007 366.50366125 + layer.39.0 11.04594849 2010.42104425 + layer.39.1 9.19037403 2039.12909901 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 647.33286029 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11425988 +BPFP 0.8881 bits/point +EBPFP 0.8881 equivalent bits/point +MSE 647.332860 +---------------------- --------------------------------------------------------- +Time: 22.479s Load: 1.252s, Pack+Encode: 7.932s, Decode+Unpack: 13.295s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 647.3329 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001184-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.254s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 979,612B, BPFP=0.6091 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 933,860B, BPFP=0.5807 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,722,156B, BPFP=1.0709 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,684,996B, BPFP=1.0478 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,916,460B, BPFP=1.1917 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,860,244B, BPFP=1.1567 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,286,516B, BPFP=0.8000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,240,068B, BPFP=0.7711 +⌛️ [2/4] FRONTEND: Frontend time: 7.993s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.376s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 68.82254855 + layer.9.1 0.14317998 53.54852157 + layer.19.0 0.15093802 142.69855341 + layer.19.1 0.13472426 112.13560570 + layer.29.0 0.10723148 145.01439629 + layer.29.1 0.10832139 134.07451847 + layer.39.0 40.62415433 1804.28334925 + layer.39.1 9.85226018 1824.85466412 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 535.67901967 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11623912 +BPFP 0.9035 bits/point +EBPFP 0.9035 equivalent bits/point +MSE 535.679020 +---------------------- --------------------------------------------------------- +Time: 22.623s Load: 1.254s, Pack+Encode: 7.993s, Decode+Unpack: 13.376s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 535.6790 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001198-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.251s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 873,636B, BPFP=0.5432 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 885,264B, BPFP=0.5505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,616,452B, BPFP=1.0051 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,635,444B, BPFP=1.0169 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,799,916B, BPFP=1.1192 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,840,604B, BPFP=1.1445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,219,784B, BPFP=0.7585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,219,976B, BPFP=0.7586 +⌛️ [2/4] FRONTEND: Frontend time: 7.979s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.184s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 16.24750527 + layer.9.1 0.03106517 12.58091696 + layer.19.0 0.04795660 215.58351242 + layer.19.1 0.11462555 156.30213308 + layer.29.0 4.19919699 280.26448583 + layer.29.1 4.19569772 238.68931073 + layer.39.0 34.63583701 1926.03979624 + layer.39.1 33.06685271 1913.34081503 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 594.88105944 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11091076 +BPFP 0.8621 bits/point +EBPFP 0.8621 equivalent bits/point +MSE 594.881059 +---------------------- --------------------------------------------------------- +Time: 22.413s Load: 1.251s, Pack+Encode: 7.979s, Decode+Unpack: 13.184s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 594.8811 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001272-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 975,908B, BPFP=0.6068 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,009,416B, BPFP=0.6277 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,737,416B, BPFP=1.0804 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,783,624B, BPFP=1.1091 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,962,776B, BPFP=1.2205 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,035,948B, BPFP=1.2660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,231,116B, BPFP=0.7655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,267,012B, BPFP=0.7878 +⌛️ [2/4] FRONTEND: Frontend time: 7.964s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.245s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 69.71189510 + layer.9.1 0.14287666 73.21334766 + layer.19.0 0.11209038 30.29111449 + layer.19.1 0.11164490 33.97090646 + layer.29.0 0.12578187 283.21601401 + layer.29.1 0.11401374 319.63270057 + layer.39.0 22.42121339 2017.72381407 + layer.39.1 25.87191330 2107.55523719 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 616.91437869 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12003216 +BPFP 0.9330 bits/point +EBPFP 0.9330 equivalent bits/point +MSE 616.914379 +---------------------- --------------------------------------------------------- +Time: 22.460s Load: 1.250s, Pack+Encode: 7.964s, Decode+Unpack: 13.245s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 616.9144 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001342-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 924,948B, BPFP=0.5751 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 966,764B, BPFP=0.6011 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,653,804B, BPFP=1.0284 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,695,636B, BPFP=1.0544 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,774,648B, BPFP=1.1035 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,834,936B, BPFP=1.1410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,144,480B, BPFP=0.7117 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,167,852B, BPFP=0.7262 +⌛️ [2/4] FRONTEND: Frontend time: 7.964s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.231s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 40.57421999 + layer.9.1 0.00120738 41.47655255 + layer.19.0 0.01953576 103.62791507 + layer.19.1 0.08568942 152.75382044 + layer.29.0 0.14491542 222.21356654 + layer.29.1 0.15694472 403.97397326 + layer.39.0 8.88920166 1620.10760904 + layer.39.1 9.38273353 1854.01766953 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 554.84316580 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11163068 +BPFP 0.8677 bits/point +EBPFP 0.8677 equivalent bits/point +MSE 554.843166 +---------------------- --------------------------------------------------------- +Time: 22.444s Load: 1.249s, Pack+Encode: 7.964s, Decode+Unpack: 13.231s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 554.8432 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001421-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,186,008B, BPFP=0.7375 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,170,316B, BPFP=0.7277 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,791,860B, BPFP=1.1142 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,790,128B, BPFP=1.1131 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,938,660B, BPFP=1.2055 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,922,732B, BPFP=1.1956 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,212,480B, BPFP=0.7539 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,210,404B, BPFP=0.7526 +⌛️ [2/4] FRONTEND: Frontend time: 7.923s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.278s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 453.12169691 + layer.9.1 0.14739036 523.12901942 + layer.19.0 0.16044666 456.63108883 + layer.19.1 0.14398357 409.90027061 + layer.29.0 0.50679369 482.79524833 + layer.29.1 0.43405572 435.93549029 + layer.39.0 123.83094556 2045.02355938 + layer.39.1 72.08861628 2030.26599809 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 854.60029648 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12222588 +BPFP 0.9500 bits/point +EBPFP 0.9500 equivalent bits/point +MSE 854.600296 +---------------------- --------------------------------------------------------- +Time: 22.445s Load: 1.245s, Pack+Encode: 7.923s, Decode+Unpack: 13.278s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 854.6003 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001428-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 926,624B, BPFP=0.5762 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 948,660B, BPFP=0.5899 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,563,168B, BPFP=0.9720 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,567,272B, BPFP=0.9746 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,513,056B, BPFP=0.9408 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,517,564B, BPFP=0.9436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 934,504B, BPFP=0.5811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 958,224B, BPFP=0.5958 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.199s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 94.92945121 + layer.9.1 0.14229169 113.18982808 + layer.19.0 0.04567823 236.07746339 + layer.19.1 0.04432558 221.61668259 + layer.29.0 0.11507784 127.41174188 + layer.29.1 0.11363094 156.06527579 + layer.39.0 38.15331751 2066.12718879 + layer.39.1 50.78157832 2198.43600764 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 651.73170492 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9929072 +BPFP 0.7718 bits/point +EBPFP 0.7718 equivalent bits/point +MSE 651.731705 +---------------------- --------------------------------------------------------- +Time: 22.318s Load: 1.242s, Pack+Encode: 7.878s, Decode+Unpack: 13.199s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 651.7317 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001439-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,165,212B, BPFP=0.7245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,204,728B, BPFP=0.7491 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,765,896B, BPFP=1.0981 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,796,144B, BPFP=1.1169 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,861,816B, BPFP=1.1577 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,911,944B, BPFP=1.1889 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,035,884B, BPFP=0.6441 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,058,824B, BPFP=0.6584 +⌛️ [2/4] FRONTEND: Frontend time: 7.875s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.244s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 369.11274276 + layer.9.1 0.14417255 416.65874721 + layer.19.0 0.04986641 112.59479465 + layer.19.1 0.03935205 233.22043139 + layer.29.0 4.19438972 72.18352535 + layer.29.1 0.10069272 77.07973675 + layer.39.0 8.54645341 1559.09821713 + layer.39.1 8.58293537 1741.28080229 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 572.65362469 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11800448 +BPFP 0.9172 bits/point +EBPFP 0.9172 equivalent bits/point +MSE 572.653625 +---------------------- --------------------------------------------------------- +Time: 22.361s Load: 1.241s, Pack+Encode: 7.875s, Decode+Unpack: 13.244s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.6536 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001452-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 913,084B, BPFP=0.5678 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 910,592B, BPFP=0.5662 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,721,460B, BPFP=1.0704 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,730,536B, BPFP=1.0761 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,917,584B, BPFP=1.1924 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,949,196B, BPFP=1.2120 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,156,040B, BPFP=0.7188 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,233,848B, BPFP=0.7672 +⌛️ [2/4] FRONTEND: Frontend time: 7.913s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.261s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 68.76744070 + layer.9.1 0.14191958 57.08583055 + layer.19.0 0.11064845 70.71293975 + layer.19.1 0.11258393 80.02338527 + layer.29.0 0.14067722 231.47150589 + layer.29.1 0.15898021 278.75449698 + layer.39.0 18.90648132 1844.87169691 + layer.39.1 12.01175482 1949.17574021 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 572.60787953 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11532340 +BPFP 0.8964 bits/point +EBPFP 0.8964 equivalent bits/point +MSE 572.607880 +---------------------- --------------------------------------------------------- +Time: 22.438s Load: 1.265s, Pack+Encode: 7.913s, Decode+Unpack: 13.261s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.6079 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001464-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.250s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 924,028B, BPFP=0.5746 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 903,772B, BPFP=0.5620 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,729,252B, BPFP=1.0753 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,715,232B, BPFP=1.0666 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,905,940B, BPFP=1.1851 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,893,276B, BPFP=1.1773 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,221,732B, BPFP=0.7597 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,212,140B, BPFP=0.7537 +⌛️ [2/4] FRONTEND: Frontend time: 7.944s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.275s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 16.74579528 + layer.9.1 0.03265336 12.57735519 + layer.19.0 0.11338584 111.35622612 + layer.19.1 0.11737041 106.81786254 + layer.29.0 0.14518043 265.64306749 + layer.29.1 0.15176190 152.00424825 + layer.39.0 10.84722720 1787.96657116 + layer.39.1 10.76635501 1614.17733206 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 508.41105726 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11505372 +BPFP 0.8943 bits/point +EBPFP 0.8943 equivalent bits/point +MSE 508.411057 +---------------------- --------------------------------------------------------- +Time: 22.470s Load: 1.250s, Pack+Encode: 7.944s, Decode+Unpack: 13.275s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 508.4111 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001478-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 999,440B, BPFP=0.6215 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,019,948B, BPFP=0.6342 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,743,016B, BPFP=1.0838 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,742,344B, BPFP=1.0834 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,911,436B, BPFP=1.1886 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,881,144B, BPFP=1.1697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,225,288B, BPFP=0.7619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,238,368B, BPFP=0.7700 +⌛️ [2/4] FRONTEND: Frontend time: 7.984s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.278s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 56.57559794 + layer.9.1 0.14310633 45.68097342 + layer.19.0 0.11868409 201.96860076 + layer.19.1 0.12162521 188.66919373 + layer.29.0 0.16395149 369.35868354 + layer.29.1 0.12259847 231.31821076 + layer.39.0 330.19024594 2852.69850366 + layer.39.1 213.90321554 2581.67749124 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 815.99340688 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11760984 +BPFP 0.9141 bits/point +EBPFP 0.9141 equivalent bits/point +MSE 815.993407 +---------------------- --------------------------------------------------------- +Time: 22.505s Load: 1.243s, Pack+Encode: 7.984s, Decode+Unpack: 13.278s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 815.9934 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001495-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 942,896B, BPFP=0.5863 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 972,500B, BPFP=0.6047 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,585,116B, BPFP=0.9857 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,616,188B, BPFP=1.0050 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,702,260B, BPFP=1.0585 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,769,968B, BPFP=1.1006 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,019,232B, BPFP=0.6338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,033,416B, BPFP=0.6426 +⌛️ [2/4] FRONTEND: Frontend time: 7.897s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.211s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 197.16935291 + layer.9.1 0.14187113 175.39121697 + layer.19.0 0.03719415 67.56750438 + layer.19.1 0.03715970 58.62743255 + layer.29.0 0.14992467 293.93188873 + layer.29.1 0.21581549 363.57175263 + layer.39.0 54.12547258 1738.74180197 + layer.39.1 37.28096148 1841.18178924 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 592.02284242 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10641576 +BPFP 0.8271 bits/point +EBPFP 0.8271 equivalent bits/point +MSE 592.022842 +---------------------- --------------------------------------------------------- +Time: 22.351s Load: 1.243s, Pack+Encode: 7.897s, Decode+Unpack: 13.211s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 592.0228 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,161,880B, BPFP=0.7225 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,169,100B, BPFP=0.7270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,842,352B, BPFP=1.1456 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,846,192B, BPFP=1.1480 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,021,528B, BPFP=1.2570 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,018,228B, BPFP=1.2550 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,294,040B, BPFP=0.8047 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,272,352B, BPFP=0.7912 +⌛️ [2/4] FRONTEND: Frontend time: 7.975s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.318s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 274.51094397 + layer.9.1 0.14222666 258.91567176 + layer.19.0 0.12883153 266.72540592 + layer.19.1 0.12450899 357.44126074 + layer.29.0 0.12456659 245.08623846 + layer.29.1 0.12180437 218.44804600 + layer.39.0 16.93397679 2112.59614772 + layer.39.1 11.63264585 2141.01862464 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 734.34279240 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12625672 +BPFP 0.9814 bits/point +EBPFP 0.9814 equivalent bits/point +MSE 734.342792 +---------------------- --------------------------------------------------------- +Time: 22.540s Load: 1.247s, Pack+Encode: 7.975s, Decode+Unpack: 13.318s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 734.3428 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001520-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.248s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 852,252B, BPFP=0.5299 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 843,860B, BPFP=0.5247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,488,608B, BPFP=0.9256 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,481,940B, BPFP=0.9215 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,521,388B, BPFP=0.9460 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,531,496B, BPFP=0.9523 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 956,728B, BPFP=0.5949 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,001,620B, BPFP=0.6228 +⌛️ [2/4] FRONTEND: Frontend time: 7.946s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.149s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 102.58528335 + layer.9.1 0.14320703 117.84876433 + layer.19.0 0.18609190 292.40413085 + layer.19.1 0.20413370 368.90739414 + layer.29.0 0.16595908 231.79779529 + layer.29.1 0.17797341 287.42846625 + layer.39.0 9.44991518 1571.04504935 + layer.39.1 9.33992148 1513.25405922 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 560.65886785 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9677892 +BPFP 0.7522 bits/point +EBPFP 0.7522 equivalent bits/point +MSE 560.658868 +---------------------- --------------------------------------------------------- +Time: 22.343s Load: 1.248s, Pack+Encode: 7.946s, Decode+Unpack: 13.149s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 560.6589 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001571-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 958,944B, BPFP=0.5963 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 962,808B, BPFP=0.5987 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,521,936B, BPFP=0.9464 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,515,368B, BPFP=0.9423 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,621,796B, BPFP=1.0085 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,618,524B, BPFP=1.0064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 916,136B, BPFP=0.5697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 931,204B, BPFP=0.5790 +⌛️ [2/4] FRONTEND: Frontend time: 7.910s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.189s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 206.34274514 + layer.9.1 0.14264699 282.61003661 + layer.19.0 0.04840791 153.18023719 + layer.19.1 0.04358378 154.85390799 + layer.29.0 4.25626169 157.78871180 + layer.29.1 4.25716892 126.63880930 + layer.39.0 36.32893585 1552.69818529 + layer.39.1 22.75239275 1465.00175103 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 512.38929804 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10046716 +BPFP 0.7809 bits/point +EBPFP 0.7809 equivalent bits/point +MSE 512.389298 +---------------------- --------------------------------------------------------- +Time: 22.342s Load: 1.243s, Pack+Encode: 7.910s, Decode+Unpack: 13.189s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 512.3893 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001605-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,072,920B, BPFP=0.6672 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,090,636B, BPFP=0.6782 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,770,888B, BPFP=1.1012 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,757,560B, BPFP=1.0929 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,910,648B, BPFP=1.1881 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,893,352B, BPFP=1.1773 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,263,772B, BPFP=0.7858 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,233,836B, BPFP=0.7672 +⌛️ [2/4] FRONTEND: Frontend time: 7.971s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.344s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 134.88700852 + layer.9.1 0.14259219 166.24448822 + layer.19.0 0.15398767 309.12420408 + layer.19.1 0.14449470 286.68833572 + layer.29.0 0.17467273 399.58930277 + layer.29.1 0.17545724 284.15263849 + layer.39.0 16.22751761 1913.22238141 + layer.39.1 26.19674268 2021.18513212 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 689.38668642 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11993612 +BPFP 0.9322 bits/point +EBPFP 0.9322 equivalent bits/point +MSE 689.386686 +---------------------- --------------------------------------------------------- +Time: 22.558s Load: 1.244s, Pack+Encode: 7.971s, Decode+Unpack: 13.344s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 689.3867 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001617-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,140,720B, BPFP=0.7093 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,149,172B, BPFP=0.7146 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,857,544B, BPFP=1.1551 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,871,664B, BPFP=1.1638 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,089,636B, BPFP=1.2994 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,115,768B, BPFP=1.3156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,346,076B, BPFP=0.8370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,404,884B, BPFP=0.8736 +⌛️ [2/4] FRONTEND: Frontend time: 8.032s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 154.24228948 + layer.9.1 0.14283950 155.19221585 + layer.19.0 0.09585176 199.45528892 + layer.19.1 0.13229247 294.73272843 + layer.29.0 0.10926771 54.88809296 + layer.29.1 0.10983113 88.89135626 + layer.39.0 13.84559555 1963.92136262 + layer.39.1 12.75833856 2142.07290672 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 631.67453016 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12975464 +BPFP 1.0085 bits/point +EBPFP 1.0085 equivalent bits/point +MSE 631.674530 +---------------------- --------------------------------------------------------- +Time: 22.676s Load: 1.244s, Pack+Encode: 8.032s, Decode+Unpack: 13.400s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 631.6745 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001630-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.248s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,285,800B, BPFP=0.7995 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,154,352B, BPFP=0.7178 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,886,900B, BPFP=1.1733 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,808,768B, BPFP=1.1247 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,013,600B, BPFP=1.2521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,977,880B, BPFP=1.2299 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,269,060B, BPFP=0.7891 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,275,264B, BPFP=0.7930 +⌛️ [2/4] FRONTEND: Frontend time: 7.932s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.815s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 560.57433938 + layer.9.1 0.14345678 293.39812162 + layer.19.0 0.16166856 309.62109997 + layer.19.1 0.14880180 353.09455587 + layer.29.0 0.17070711 207.87145813 + layer.29.1 0.15868870 336.86405603 + layer.39.0 31.98565594 2073.56988220 + layer.39.1 38.57007372 2136.27730022 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 783.90885168 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12671624 +BPFP 0.9849 bits/point +EBPFP 0.9849 equivalent bits/point +MSE 783.908852 +---------------------- --------------------------------------------------------- +Time: 21.995s Load: 1.248s, Pack+Encode: 7.932s, Decode+Unpack: 12.815s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 783.9089 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001636-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 875,788B, BPFP=0.5446 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 863,880B, BPFP=0.5372 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,596,568B, BPFP=0.9928 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,600,712B, BPFP=0.9953 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,687,840B, BPFP=1.0495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,689,184B, BPFP=1.0504 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,100,676B, BPFP=0.6844 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,133,948B, BPFP=0.7051 +⌛️ [2/4] FRONTEND: Frontend time: 7.881s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 12.717s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 12.85677830 + layer.9.1 0.03218400 36.81719098 + layer.19.0 0.03742503 76.13099928 + layer.19.1 0.04139693 117.09195917 + layer.29.0 0.11425402 158.64121697 + layer.29.1 0.11776626 153.21356654 + layer.39.0 23.31748448 1942.69866285 + layer.39.1 15.89369429 1793.39286851 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 536.35540532 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10548596 +BPFP 0.8199 bits/point +EBPFP 0.8199 equivalent bits/point +MSE 536.355405 +---------------------- --------------------------------------------------------- +Time: 21.847s Load: 1.249s, Pack+Encode: 7.881s, Decode+Unpack: 12.717s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 536.3554 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001639-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.248s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,110,788B, BPFP=0.6907 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,123,584B, BPFP=0.6987 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,738,180B, BPFP=1.0808 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,747,408B, BPFP=1.0866 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,753,048B, BPFP=1.0901 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,772,840B, BPFP=1.1024 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 965,492B, BPFP=0.6004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 960,692B, BPFP=0.5974 +⌛️ [2/4] FRONTEND: Frontend time: 7.926s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.159s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 331.48694683 + layer.9.1 0.14315520 284.28255333 + layer.19.0 0.04114968 87.36718999 + layer.19.1 0.04120060 122.08173153 + layer.29.0 0.18627036 491.18847501 + layer.29.1 0.17990809 509.33508437 + layer.39.0 46.02158449 1909.07879656 + layer.39.1 44.38447151 1914.76599809 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 706.19834696 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11172032 +BPFP 0.8684 bits/point +EBPFP 0.8684 equivalent bits/point +MSE 706.198347 +---------------------- --------------------------------------------------------- +Time: 22.332s Load: 1.248s, Pack+Encode: 7.926s, Decode+Unpack: 13.159s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 706.1983 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001653-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 829,360B, BPFP=0.5157 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 852,076B, BPFP=0.5298 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,651,704B, BPFP=1.0271 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,671,388B, BPFP=1.0393 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,872,092B, BPFP=1.1641 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,886,620B, BPFP=1.1731 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,206,732B, BPFP=0.7504 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,215,848B, BPFP=0.7560 +⌛️ [2/4] FRONTEND: Frontend time: 7.996s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.263s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64482133 36.29194524 + layer.9.1 0.03141260 24.70013531 + layer.19.0 3.18767318 52.82308580 + layer.19.1 3.18914595 61.73725028 + layer.29.0 4.14946039 36.83507193 + layer.29.1 4.13952905 52.30571474 + layer.39.0 7.50609877 1501.87297039 + layer.39.1 7.79272438 1521.37106017 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 410.99215423 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11185820 +BPFP 0.8694 bits/point +EBPFP 0.8694 equivalent bits/point +MSE 410.992154 +---------------------- --------------------------------------------------------- +Time: 22.503s Load: 1.244s, Pack+Encode: 7.996s, Decode+Unpack: 13.263s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 410.9922 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,026,956B, BPFP=0.6386 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 998,824B, BPFP=0.6211 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,741,296B, BPFP=1.0828 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,729,876B, BPFP=1.0757 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,941,360B, BPFP=1.2072 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,952,144B, BPFP=1.2139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,163,072B, BPFP=0.7232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,160,860B, BPFP=0.7218 +⌛️ [2/4] FRONTEND: Frontend time: 7.959s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.198s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 238.22777380 + layer.9.1 0.14140505 149.89169452 + layer.19.0 0.11753838 139.17515322 + layer.19.1 0.11213660 80.45986549 + layer.29.0 0.21817993 431.68051576 + layer.29.1 4.26279853 365.40687679 + layer.39.0 8.71778059 1752.79226361 + layer.39.1 8.43609532 1740.60076409 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 612.27936341 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11714388 +BPFP 0.9105 bits/point +EBPFP 0.9105 equivalent bits/point +MSE 612.279363 +---------------------- --------------------------------------------------------- +Time: 22.400s Load: 1.243s, Pack+Encode: 7.959s, Decode+Unpack: 13.198s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 612.2794 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001659-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,070,216B, BPFP=0.6655 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,153,244B, BPFP=0.7171 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,670,256B, BPFP=1.0386 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,764,280B, BPFP=1.0971 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,732,624B, BPFP=1.0774 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,881,528B, BPFP=1.1700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,112,036B, BPFP=0.6915 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,214,896B, BPFP=0.7554 +⌛️ [2/4] FRONTEND: Frontend time: 8.000s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.361s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 230.56594238 + layer.9.1 0.11967093 329.79166667 + layer.19.0 0.14332279 133.41068728 + layer.19.1 0.14205440 169.07018067 + layer.29.0 0.15356100 133.17556113 + layer.29.1 0.14462723 133.63388451 + layer.39.0 8.04224558 1557.58962114 + layer.39.1 10.17930073 1632.37169691 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 539.95115509 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11599080 +BPFP 0.9016 bits/point +EBPFP 0.9016 equivalent bits/point +MSE 539.951155 +---------------------- --------------------------------------------------------- +Time: 22.607s Load: 1.246s, Pack+Encode: 8.000s, Decode+Unpack: 13.361s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 539.9512 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001671-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 870,072B, BPFP=0.5410 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 867,124B, BPFP=0.5392 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,543,008B, BPFP=0.9595 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,534,208B, BPFP=0.9540 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,656,880B, BPFP=1.0303 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,609,700B, BPFP=1.0009 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 961,368B, BPFP=0.5978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 967,192B, BPFP=0.6014 +⌛️ [2/4] FRONTEND: Frontend time: 7.929s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.088s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 53.09587910 + layer.9.1 0.00091860 28.99087174 + layer.19.0 3.15620088 29.75977495 + layer.19.1 3.15238324 70.71939669 + layer.29.0 4.13387767 36.59139705 + layer.29.1 4.13737010 31.71323076 + layer.39.0 41.03603550 1770.19277300 + layer.39.1 41.15380502 1801.01480420 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 477.75976594 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10009552 +BPFP 0.7780 bits/point +EBPFP 0.7780 equivalent bits/point +MSE 477.759766 +---------------------- --------------------------------------------------------- +Time: 22.267s Load: 1.249s, Pack+Encode: 7.929s, Decode+Unpack: 13.088s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 477.7598 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001694-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 986,136B, BPFP=0.6132 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,014,408B, BPFP=0.6308 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,601,668B, BPFP=0.9959 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,672,212B, BPFP=1.0398 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,741,864B, BPFP=1.0831 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,874,396B, BPFP=1.1655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,103,524B, BPFP=0.6862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,147,212B, BPFP=0.7134 +⌛️ [2/4] FRONTEND: Frontend time: 7.970s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.325s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 245.78122015 + layer.9.1 0.14279730 242.78784623 + layer.19.0 0.12708100 262.79274117 + layer.19.1 0.11978473 180.70743792 + layer.29.0 0.14591184 389.70717924 + layer.29.1 0.16402206 520.56518625 + layer.39.0 105.60261461 2207.62862146 + layer.39.1 191.64541547 2626.12957657 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 834.51247612 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11141420 +BPFP 0.8660 bits/point +EBPFP 0.8660 equivalent bits/point +MSE 834.512476 +---------------------- --------------------------------------------------------- +Time: 22.546s Load: 1.252s, Pack+Encode: 7.970s, Decode+Unpack: 13.325s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 834.5125 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001712-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.244s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 996,900B, BPFP=0.6199 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 959,776B, BPFP=0.5968 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,758,984B, BPFP=1.0938 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,719,288B, BPFP=1.0691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,959,080B, BPFP=1.2182 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,908,340B, BPFP=1.1866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,245,232B, BPFP=0.7743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,269,500B, BPFP=0.7894 +⌛️ [2/4] FRONTEND: Frontend time: 7.935s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.342s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 53.77297736 + layer.9.1 0.14187527 69.50906857 + layer.19.0 0.05966252 160.31688754 + layer.19.1 0.05602499 156.88890879 + layer.29.0 0.10851584 81.09724212 + layer.29.1 0.10663395 95.26856495 + layer.39.0 36.66006795 2501.34606813 + layer.39.1 37.39855191 2314.27793696 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 679.05970680 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11817100 +BPFP 0.9185 bits/point +EBPFP 0.9185 equivalent bits/point +MSE 679.059707 +---------------------- --------------------------------------------------------- +Time: 22.521s Load: 1.244s, Pack+Encode: 7.935s, Decode+Unpack: 13.342s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 679.0597 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001750-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,008,208B, BPFP=0.6269 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 994,628B, BPFP=0.6185 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,735,736B, BPFP=1.0793 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,742,288B, BPFP=1.0834 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,797,484B, BPFP=1.1177 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,848,520B, BPFP=1.1494 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,075,672B, BPFP=0.6689 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,115,456B, BPFP=0.6936 +⌛️ [2/4] FRONTEND: Frontend time: 7.933s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.265s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 73.32496617 + layer.9.1 0.11247108 8.61639655 + layer.19.0 0.01001183 151.46509869 + layer.19.1 3.17262087 170.32274753 + layer.29.0 0.16690336 129.92820758 + layer.29.1 0.17317613 146.14229147 + layer.39.0 33.55914965 1938.19993633 + layer.39.1 10.63762287 1996.10219675 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 576.76273013 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11317992 +BPFP 0.8797 bits/point +EBPFP 0.8797 equivalent bits/point +MSE 576.762730 +---------------------- --------------------------------------------------------- +Time: 22.447s Load: 1.249s, Pack+Encode: 7.933s, Decode+Unpack: 13.265s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 576.7627 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 983,456B, BPFP=0.6115 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,062,884B, BPFP=0.6609 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,654,216B, BPFP=1.0286 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,727,332B, BPFP=1.0741 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,798,500B, BPFP=1.1183 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,907,096B, BPFP=1.1859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,156,584B, BPFP=0.7192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,200,140B, BPFP=0.7463 +⌛️ [2/4] FRONTEND: Frontend time: 7.907s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.350s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 57.33303984 + layer.9.1 0.03247940 57.37971088 + layer.19.0 0.20408508 399.92398918 + layer.19.1 0.20919449 434.79313913 + layer.29.0 0.13400092 264.97697787 + layer.29.1 0.12260655 284.22289876 + layer.39.0 13.98719058 1776.87567654 + layer.39.1 8.64389327 1797.95256288 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 634.18224938 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11490208 +BPFP 0.8931 bits/point +EBPFP 0.8931 equivalent bits/point +MSE 634.182249 +---------------------- --------------------------------------------------------- +Time: 22.509s Load: 1.252s, Pack+Encode: 7.907s, Decode+Unpack: 13.350s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 634.1822 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001838-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.253s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,062,696B, BPFP=0.6608 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,070,816B, BPFP=0.6659 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,741,236B, BPFP=1.0827 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,762,128B, BPFP=1.0957 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,853,240B, BPFP=1.1524 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,861,288B, BPFP=1.1574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,170,356B, BPFP=0.7277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,152,120B, BPFP=0.7164 +⌛️ [2/4] FRONTEND: Frontend time: 7.963s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.244s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 168.10130134 + layer.9.1 0.14463072 207.45570678 + layer.19.0 0.16931463 270.01876393 + layer.19.1 0.17979540 297.55074021 + layer.29.0 0.11737749 186.06669850 + layer.29.1 0.10948915 143.48251950 + layer.39.0 8.46774266 1511.79258198 + layer.39.1 8.48397517 1520.50334288 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 538.12145689 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11673880 +BPFP 0.9074 bits/point +EBPFP 0.9074 equivalent bits/point +MSE 538.121457 +---------------------- --------------------------------------------------------- +Time: 22.460s Load: 1.253s, Pack+Encode: 7.963s, Decode+Unpack: 13.244s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 538.1215 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001840-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,060,124B, BPFP=0.6592 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,070,088B, BPFP=0.6654 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,787,332B, BPFP=1.1114 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,791,488B, BPFP=1.1140 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,006,592B, BPFP=1.2477 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,012,960B, BPFP=1.2517 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,251,688B, BPFP=0.7783 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,234,192B, BPFP=0.7674 +⌛️ [2/4] FRONTEND: Frontend time: 7.977s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.303s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14223057 193.44719039 + layer.9.1 0.14268742 198.70586597 + layer.19.0 0.21739516 450.09992837 + layer.19.1 0.24972380 472.91487584 + layer.29.0 0.18828982 474.11903056 + layer.29.1 0.18108670 512.70534862 + layer.39.0 11.67542184 2105.64151544 + layer.39.1 15.11985385 2161.83683540 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 821.18382382 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12214464 +BPFP 0.9494 bits/point +EBPFP 0.9494 equivalent bits/point +MSE 821.183824 +---------------------- --------------------------------------------------------- +Time: 22.527s Load: 1.247s, Pack+Encode: 7.977s, Decode+Unpack: 13.303s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 821.1838 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001854-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.245s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 997,340B, BPFP=0.6202 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 997,648B, BPFP=0.6204 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,732,112B, BPFP=1.0771 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,716,808B, BPFP=1.0675 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,924,012B, BPFP=1.1964 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,896,820B, BPFP=1.1795 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,155,444B, BPFP=0.7185 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,152,188B, BPFP=0.7164 +⌛️ [2/4] FRONTEND: Frontend time: 7.862s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.282s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 105.20304043 + layer.9.1 0.14270393 117.32051894 + layer.19.0 0.11367196 162.12786533 + layer.19.1 0.12267420 147.80944564 + layer.29.0 0.13560262 226.67012894 + layer.29.1 0.14809222 267.80726679 + layer.39.0 10.32325245 1798.60888252 + layer.39.1 8.35688960 1737.08086597 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 570.32850182 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11572372 +BPFP 0.8995 bits/point +EBPFP 0.8995 equivalent bits/point +MSE 570.328502 +---------------------- --------------------------------------------------------- +Time: 22.389s Load: 1.245s, Pack+Encode: 7.862s, Decode+Unpack: 13.282s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 570.3285 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001855-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 842,440B, BPFP=0.5238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 849,304B, BPFP=0.5281 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,563,084B, BPFP=0.9720 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,596,156B, BPFP=0.9925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,640,328B, BPFP=1.0200 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,701,052B, BPFP=1.0577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,075,980B, BPFP=0.6691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,079,892B, BPFP=0.6715 +⌛️ [2/4] FRONTEND: Frontend time: 7.942s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.201s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61171023 28.92721018 + layer.9.1 2.72679972 25.00130830 + layer.19.0 0.11263356 122.81158469 + layer.19.1 0.10212393 150.34567017 + layer.29.0 4.19513435 109.23709607 + layer.29.1 4.21594343 146.64050064 + layer.39.0 8.80532175 1565.77363897 + layer.39.1 9.27097449 1747.66650748 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 487.05043956 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10348236 +BPFP 0.8043 bits/point +EBPFP 0.8043 equivalent bits/point +MSE 487.050440 +---------------------- --------------------------------------------------------- +Time: 22.385s Load: 1.242s, Pack+Encode: 7.942s, Decode+Unpack: 13.201s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 487.0504 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001857-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.254s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,087,864B, BPFP=0.6765 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,114,132B, BPFP=0.6928 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,706,628B, BPFP=1.0612 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,748,992B, BPFP=1.0876 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,830,432B, BPFP=1.1382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,883,736B, BPFP=1.1713 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,121,552B, BPFP=0.6974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,123,416B, BPFP=0.6986 +⌛️ [2/4] FRONTEND: Frontend time: 7.962s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.238s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 307.79582537 + layer.9.1 0.14997165 288.58966094 + layer.19.0 0.15685862 284.92158150 + layer.19.1 0.13652294 213.16141356 + layer.29.0 0.22636045 386.73953359 + layer.29.1 0.21023706 399.49442853 + layer.39.0 31.35143565 1955.37360713 + layer.39.1 33.65704095 2145.23432028 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 747.66379636 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11616752 +BPFP 0.9029 bits/point +EBPFP 0.9029 equivalent bits/point +MSE 747.663796 +---------------------- --------------------------------------------------------- +Time: 22.455s Load: 1.254s, Pack+Encode: 7.962s, Decode+Unpack: 13.238s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 747.6638 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 978,868B, BPFP=0.6087 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,003,968B, BPFP=0.6243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,688,204B, BPFP=1.0498 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,704,392B, BPFP=1.0598 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,896,516B, BPFP=1.1793 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,912,228B, BPFP=1.1891 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,215,224B, BPFP=0.7556 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,189,852B, BPFP=0.7399 +⌛️ [2/4] FRONTEND: Frontend time: 7.956s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.339s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 149.79849172 + layer.9.1 0.14194651 89.01276465 + layer.19.0 0.13165920 271.09449618 + layer.19.1 0.11547583 311.75702404 + layer.29.0 4.19202371 272.79757641 + layer.29.1 0.11136677 197.05587393 + layer.39.0 9.51575185 1846.93744031 + layer.39.1 9.66679849 1860.02594715 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 624.80995180 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11589252 +BPFP 0.9008 bits/point +EBPFP 0.9008 equivalent bits/point +MSE 624.809952 +---------------------- --------------------------------------------------------- +Time: 22.542s Load: 1.247s, Pack+Encode: 7.956s, Decode+Unpack: 13.339s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 624.8100 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 807,596B, BPFP=0.5022 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 802,132B, BPFP=0.4988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,474,344B, BPFP=0.9168 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,465,832B, BPFP=0.9115 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,365,120B, BPFP=0.8489 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,359,604B, BPFP=0.8454 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 839,668B, BPFP=0.5221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 845,656B, BPFP=0.5258 +⌛️ [2/4] FRONTEND: Frontend time: 7.896s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.158s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.60361947 64.75165652 + layer.9.1 2.64162177 76.70065763 + layer.19.0 3.15421573 86.12634312 + layer.19.1 3.18597002 104.80309814 + layer.29.0 4.16148507 62.64850466 + layer.29.1 4.16879732 48.02800661 + layer.39.0 7.32495125 1371.13467049 + layer.39.1 7.16856507 1258.15934416 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 384.04403516 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8959952 +BPFP 0.6964 bits/point +EBPFP 0.6964 equivalent bits/point +MSE 384.044035 +---------------------- --------------------------------------------------------- +Time: 22.300s Load: 1.246s, Pack+Encode: 7.896s, Decode+Unpack: 13.158s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 384.0440 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.8784 bits/point +Avg EBPFP 0.8784 equivalent bits/point +Avg MSE 601.755951 +Avg Time 22.438s +------------------------ ---------------------------- diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..a31e52d1589d2c0753bbbb55cc209bd0d6da5dba --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_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/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-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 hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.254s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,791,220B, BPFP=1.1138 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,839,144B, BPFP=1.1436 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,198,848B, BPFP=1.3673 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,266,848B, BPFP=1.4096 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,284,268B, BPFP=1.4204 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,373,628B, BPFP=1.4760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,440,976B, BPFP=0.8960 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,481,924B, BPFP=0.9215 +⌛️ [2/4] FRONTEND: Frontend time: 8.349s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.777s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11100285 20.60175402 + layer.9.1 0.11103876 17.02795562 + layer.19.0 0.02553116 95.00368115 + layer.19.1 0.10833414 169.69884193 + layer.29.0 0.30844607 288.99942295 + layer.29.1 0.33610574 351.88566539 + layer.39.0 10.03071710 1387.45765680 + layer.39.1 10.11984639 1419.06765361 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 468.71782893 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15676856 +BPFP 1.2185 bits/point +EBPFP 1.2185 equivalent bits/point +MSE 468.717829 +---------------------- --------------------------------------------------------- +Time: 23.380s Load: 1.254s, Pack+Encode: 8.349s, Decode+Unpack: 13.777s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 468.7178 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000001-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.315s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,689,572B, BPFP=1.0506 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,728,696B, BPFP=1.0749 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,184,480B, BPFP=1.3583 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,252,752B, BPFP=1.4008 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,399,712B, BPFP=1.4922 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,485,260B, BPFP=1.5454 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,549,608B, BPFP=0.9636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,609,596B, BPFP=1.0009 +⌛️ [2/4] FRONTEND: Frontend time: 8.056s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.760s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61021196 4.54984169 + layer.9.1 2.61901253 4.27704279 + layer.19.0 3.15140481 24.10502229 + layer.19.1 3.16250889 29.03444365 + layer.29.0 4.15625404 53.30197389 + layer.29.1 4.15938147 52.75733743 + layer.39.0 10.95910936 1072.54624323 + layer.39.1 9.06533984 1067.60928048 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 288.52264818 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15899676 +BPFP 1.2358 bits/point +EBPFP 1.2358 equivalent bits/point +MSE 288.522648 +---------------------- --------------------------------------------------------- +Time: 23.131s Load: 1.315s, Pack+Encode: 8.056s, Decode+Unpack: 13.760s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 288.5226 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000045-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.272s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,988,952B, BPFP=1.2368 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,008,396B, BPFP=1.2489 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,500,040B, BPFP=1.5546 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,532,344B, BPFP=1.5747 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,764,824B, BPFP=1.7192 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,824,372B, BPFP=1.7562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,769,764B, BPFP=1.1005 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,810,804B, BPFP=1.1260 +⌛️ [2/4] FRONTEND: Frontend time: 8.168s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.559s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 21.01886342 + layer.9.1 0.14253284 29.21067136 + layer.19.0 0.09744245 129.75836716 + layer.19.1 0.13747554 98.86430476 + layer.29.0 4.19766265 74.51214283 + layer.29.1 4.20130152 88.49789080 + layer.39.0 38.53896798 1358.81630054 + layer.39.1 35.26563495 1324.35267431 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 390.62890190 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18199496 +BPFP 1.4146 bits/point +EBPFP 1.4146 equivalent bits/point +MSE 390.628902 +---------------------- --------------------------------------------------------- +Time: 22.999s Load: 1.272s, Pack+Encode: 8.168s, Decode+Unpack: 13.559s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 390.6289 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000064-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.259s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,799,232B, BPFP=1.1188 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,751,232B, BPFP=1.0889 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,404,708B, BPFP=1.4953 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,357,820B, BPFP=1.4661 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,728,768B, BPFP=1.6968 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,672,612B, BPFP=1.6619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,818,092B, BPFP=1.1305 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,825,524B, BPFP=1.1351 +⌛️ [2/4] FRONTEND: Frontend time: 7.878s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.178s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 4.20490868 + layer.9.1 0.03225276 4.21097201 + layer.19.0 0.11899935 56.48465855 + layer.19.1 0.11456829 69.72775887 + layer.29.0 0.13249551 100.78855261 + layer.29.1 0.12471250 124.34268545 + layer.39.0 10.78219516 1256.91085642 + layer.39.1 9.99374328 1180.91252786 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 349.69786506 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17357988 +BPFP 1.3492 bits/point +EBPFP 1.3492 equivalent bits/point +MSE 349.697865 +---------------------- --------------------------------------------------------- +Time: 22.315s Load: 1.259s, Pack+Encode: 7.878s, Decode+Unpack: 13.178s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 349.6979 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000092-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.288s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,761,436B, BPFP=1.0953 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,760,956B, BPFP=1.0950 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,258,280B, BPFP=1.4042 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,248,436B, BPFP=1.3981 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,501,380B, BPFP=1.5554 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,471,600B, BPFP=1.5369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,544,504B, BPFP=0.9604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,552,924B, BPFP=0.9656 +⌛️ [2/4] FRONTEND: Frontend time: 8.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.149s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 4.25816289 + layer.9.1 0.03227402 4.37191330 + layer.19.0 3.18865969 10.32217919 + layer.19.1 3.19251184 10.35968777 + layer.29.0 0.19572780 319.82533429 + layer.29.1 0.14992644 212.36914995 + layer.39.0 12.23891426 1173.72779370 + layer.39.1 9.64680585 1108.19723018 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 355.42893141 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16099516 +BPFP 1.2514 bits/point +EBPFP 1.2514 equivalent bits/point +MSE 355.428931 +---------------------- --------------------------------------------------------- +Time: 22.577s Load: 1.288s, Pack+Encode: 8.141s, Decode+Unpack: 13.149s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 355.4289 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000096-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.269s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,718,740B, BPFP=1.0687 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,755,384B, BPFP=1.0915 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,240,924B, BPFP=1.3934 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,273,352B, BPFP=1.4136 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,522,232B, BPFP=1.5684 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,572,788B, BPFP=1.5998 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,695,068B, BPFP=1.0540 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,703,528B, BPFP=1.0593 +⌛️ [2/4] FRONTEND: Frontend time: 7.889s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.405s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 4.24629366 + layer.9.1 0.14248663 4.23448817 + layer.19.0 0.04071400 28.93787806 + layer.19.1 0.03715074 28.97096367 + layer.29.0 4.22673132 118.89780325 + layer.29.1 4.22861263 114.79244269 + layer.39.0 10.70292353 1160.18369946 + layer.39.1 9.44238934 1170.47277937 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 328.84204354 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16482016 +BPFP 1.2811 bits/point +EBPFP 1.2811 equivalent bits/point +MSE 328.842044 +---------------------- --------------------------------------------------------- +Time: 22.563s Load: 1.269s, Pack+Encode: 7.889s, Decode+Unpack: 13.405s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 328.8420 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000133-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.284s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,004,008B, BPFP=1.2461 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,020,800B, BPFP=1.2566 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,533,632B, BPFP=1.5755 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,537,332B, BPFP=1.5778 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,757,440B, BPFP=1.7146 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,766,520B, BPFP=1.7203 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,697,888B, BPFP=1.0558 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,683,288B, BPFP=1.0467 +⌛️ [2/4] FRONTEND: Frontend time: 7.918s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.211s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 45.69516575 + layer.9.1 0.14203072 45.05214303 + layer.19.0 0.04969746 125.17399912 + layer.19.1 0.04852902 76.29790970 + layer.29.0 0.13952979 114.45069245 + layer.29.1 0.11857529 109.95114016 + layer.39.0 52.16041866 1172.00382044 + layer.39.1 64.85207736 1142.58038841 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 353.90065738 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18000908 +BPFP 1.3992 bits/point +EBPFP 1.3992 equivalent bits/point +MSE 353.900657 +---------------------- --------------------------------------------------------- +Time: 22.413s Load: 1.284s, Pack+Encode: 7.918s, Decode+Unpack: 13.211s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 353.9007 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000196-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.296s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,826,216B, BPFP=1.1356 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,821,536B, BPFP=1.1327 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,319,340B, BPFP=1.4422 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,342,364B, BPFP=1.4565 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,618,596B, BPFP=1.6283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,647,228B, BPFP=1.6461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,727,728B, BPFP=1.0743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,721,224B, BPFP=1.0703 +⌛️ [2/4] FRONTEND: Frontend time: 7.940s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 4.50842437 + layer.9.1 0.14255715 8.41487148 + layer.19.0 0.12077588 60.80355082 + layer.19.1 0.12364273 74.25651663 + layer.29.0 4.20710867 92.09984877 + layer.29.1 4.21108798 77.92159643 + layer.39.0 8.84959445 1142.64955428 + layer.39.1 9.12830806 1066.05507800 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 315.83868010 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17024232 +BPFP 1.3232 bits/point +EBPFP 1.3232 equivalent bits/point +MSE 315.838680 +---------------------- --------------------------------------------------------- +Time: 22.271s Load: 1.296s, Pack+Encode: 7.940s, Decode+Unpack: 13.036s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 315.8387 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000268-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,999,208B, BPFP=1.2431 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,013,708B, BPFP=1.2522 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,468,672B, BPFP=1.5351 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,488,000B, BPFP=1.5471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,761,656B, BPFP=1.7172 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,776,604B, BPFP=1.7265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,888,908B, BPFP=1.1746 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,922,672B, BPFP=1.1955 +⌛️ [2/4] FRONTEND: Frontend time: 8.311s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.893s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 42.10705687 + layer.9.1 0.14262173 37.43065753 + layer.19.0 0.13202983 133.90223058 + layer.19.1 0.12978742 157.29629298 + layer.29.0 0.12169007 127.23926496 + layer.29.1 0.13371499 80.08529330 + layer.39.0 71.22791309 1399.81884750 + layer.39.1 35.82807525 1489.37886024 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 433.40731299 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18319428 +BPFP 1.4239 bits/point +EBPFP 1.4239 equivalent bits/point +MSE 433.407313 +---------------------- --------------------------------------------------------- +Time: 23.360s Load: 1.155s, Pack+Encode: 8.311s, Decode+Unpack: 13.893s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 433.4073 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000315-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,795,056B, BPFP=1.1162 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,778,224B, BPFP=1.1057 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,367,340B, BPFP=1.4721 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,338,192B, BPFP=1.4539 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,694,156B, BPFP=1.6753 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,643,676B, BPFP=1.6439 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,825,992B, BPFP=1.1354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,751,220B, BPFP=1.0889 +⌛️ [2/4] FRONTEND: Frontend time: 7.936s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.042s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 8.42362790 + layer.9.1 0.14121198 4.26728432 + layer.19.0 0.08207523 83.43091372 + layer.19.1 0.11558007 69.88735673 + layer.29.0 0.16338114 262.21285021 + layer.29.1 0.15213004 181.91975088 + layer.39.0 27.31461666 1596.23527539 + layer.39.1 28.69002706 1436.64708691 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 455.37801826 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17193856 +BPFP 1.3364 bits/point +EBPFP 1.3364 equivalent bits/point +MSE 455.378018 +---------------------- --------------------------------------------------------- +Time: 22.267s Load: 1.289s, Pack+Encode: 7.936s, Decode+Unpack: 13.042s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 455.3780 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000322-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,893,528B, BPFP=1.1774 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,894,408B, BPFP=1.1780 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,475,720B, BPFP=1.5394 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,465,816B, BPFP=1.5333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,817,636B, BPFP=1.7521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,813,748B, BPFP=1.7496 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,922,708B, BPFP=1.1956 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,905,724B, BPFP=1.1850 +⌛️ [2/4] FRONTEND: Frontend time: 8.107s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.208s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 16.54336049 + layer.9.1 0.11112548 12.59089711 + layer.19.0 0.11343976 42.53425959 + layer.19.1 0.08227446 51.54050263 + layer.29.0 0.11178890 35.74186913 + layer.29.1 4.21559211 35.52616354 + layer.39.0 9.18455757 1134.41515441 + layer.39.1 8.88372284 1142.25151226 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 308.89296489 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18189288 +BPFP 1.4138 bits/point +EBPFP 1.4138 equivalent bits/point +MSE 308.892965 +---------------------- --------------------------------------------------------- +Time: 22.534s Load: 1.219s, Pack+Encode: 8.107s, Decode+Unpack: 13.208s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 308.8930 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000347-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.294s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,885,588B, BPFP=1.1725 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,850,932B, BPFP=1.1509 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,286,808B, BPFP=1.4220 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,288,380B, BPFP=1.4230 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,634,008B, BPFP=1.6379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,646,672B, BPFP=1.6457 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,644,740B, BPFP=1.0227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,668,008B, BPFP=1.0372 +⌛️ [2/4] FRONTEND: Frontend time: 7.965s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 98.80276982 + layer.9.1 0.14561824 90.65226043 + layer.19.0 0.12576092 107.57762257 + layer.19.1 0.12606844 80.97628641 + layer.29.0 0.19770402 91.76659503 + layer.29.1 0.18863435 105.20075215 + layer.39.0 84.70259273 1662.37233365 + layer.39.1 43.66404011 1619.79003502 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 482.14233189 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16905136 +BPFP 1.3140 bits/point +EBPFP 1.3140 equivalent bits/point +MSE 482.142332 +---------------------- --------------------------------------------------------- +Time: 22.302s Load: 1.294s, Pack+Encode: 7.965s, Decode+Unpack: 13.043s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 482.1423 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000352-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,723,024B, BPFP=1.0714 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,708,692B, BPFP=1.0625 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,105,168B, BPFP=1.3090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,068,176B, BPFP=1.2860 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,145,808B, BPFP=1.3343 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,076,592B, BPFP=1.2913 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,369,692B, BPFP=0.8517 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,326,412B, BPFP=0.8248 +⌛️ [2/4] FRONTEND: Frontend time: 7.661s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.432s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 26.06896689 + layer.9.1 0.14295322 36.25108942 + layer.19.0 0.05949541 66.80249124 + layer.19.1 0.07012351 53.07407573 + layer.29.0 4.21949463 37.89595023 + layer.29.1 4.23773965 89.14071952 + layer.39.0 8.48589099 1318.88021331 + layer.39.1 10.46205428 1292.14326648 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 365.03209660 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14523564 +BPFP 1.1289 bits/point +EBPFP 1.1289 equivalent bits/point +MSE 365.032097 +---------------------- --------------------------------------------------------- +Time: 22.358s Load: 1.265s, Pack+Encode: 7.661s, Decode+Unpack: 13.432s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 365.0321 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000360-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,806,016B, BPFP=1.1230 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,764,108B, BPFP=1.0970 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,171,660B, BPFP=1.3504 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,146,060B, BPFP=1.3345 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,119,340B, BPFP=1.3178 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,067,636B, BPFP=1.2857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,233,940B, BPFP=0.7673 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,212,408B, BPFP=0.7539 +⌛️ [2/4] FRONTEND: Frontend time: 7.108s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 20.92577006 + layer.9.1 0.00177230 8.46064711 + layer.19.0 0.01183476 38.05642112 + layer.19.1 0.01005667 28.73092019 + layer.29.0 4.18449569 22.39398530 + layer.29.1 4.18053255 26.12373149 + layer.39.0 7.97218927 1260.68083413 + layer.39.1 7.92115618 1331.98312639 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 342.16942947 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14521168 +BPFP 1.1287 bits/point +EBPFP 1.1287 equivalent bits/point +MSE 342.169429 +---------------------- --------------------------------------------------------- +Time: 21.388s Load: 1.222s, Pack+Encode: 7.108s, Decode+Unpack: 13.059s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 342.1694 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000389-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,783,912B, BPFP=1.1093 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,788,188B, BPFP=1.1119 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,299,972B, BPFP=1.4302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,297,472B, BPFP=1.4286 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,506,068B, BPFP=1.5583 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,517,564B, BPFP=1.5655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,644,376B, BPFP=1.0225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,628,304B, BPFP=1.0125 +⌛️ [2/4] FRONTEND: Frontend time: 7.806s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.336s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 4.25147775 + layer.9.1 0.03324844 8.35606072 + layer.19.0 0.13337831 74.92327782 + layer.19.1 0.12266011 65.88480977 + layer.29.0 4.22871927 175.41212989 + layer.29.1 4.21185188 96.03514008 + layer.39.0 10.68945623 1240.13697867 + layer.39.1 11.70080065 1173.25366125 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 354.78169199 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16465856 +BPFP 1.2798 bits/point +EBPFP 1.2798 equivalent bits/point +MSE 354.781692 +---------------------- --------------------------------------------------------- +Time: 22.432s Load: 1.291s, Pack+Encode: 7.806s, Decode+Unpack: 13.336s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 354.7817 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000429-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.279s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,829,640B, BPFP=1.1377 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,823,868B, BPFP=1.1341 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,345,076B, BPFP=1.4582 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,318,792B, BPFP=1.4419 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,544,764B, BPFP=1.5824 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,519,628B, BPFP=1.5667 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,568,564B, BPFP=0.9754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,565,796B, BPFP=0.9736 +⌛️ [2/4] FRONTEND: Frontend time: 7.927s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.422s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 49.48694683 + layer.9.1 0.14233285 49.48410140 + layer.19.0 0.14139387 131.51997771 + layer.19.1 0.13524239 116.59466531 + layer.29.0 0.16019033 124.55252109 + layer.29.1 0.14649145 105.68705229 + layer.39.0 12.41561455 1156.01146132 + layer.39.1 10.59172910 1188.37090099 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 365.21345337 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16516128 +BPFP 1.2837 bits/point +EBPFP 1.2837 equivalent bits/point +MSE 365.213453 +---------------------- --------------------------------------------------------- +Time: 22.628s Load: 1.279s, Pack+Encode: 7.927s, Decode+Unpack: 13.422s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 365.2135 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000436-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,915,584B, BPFP=1.1911 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,920,344B, BPFP=1.1941 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,376,672B, BPFP=1.4779 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,377,884B, BPFP=1.4786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,617,652B, BPFP=1.6277 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,613,836B, BPFP=1.6253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,581,760B, BPFP=0.9836 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,582,280B, BPFP=0.9839 +⌛️ [2/4] FRONTEND: Frontend time: 8.256s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.814s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 16.17430506 + layer.9.1 0.03247534 16.62245180 + layer.19.0 0.03739121 15.21090890 + layer.19.1 0.03736199 5.81534978 + layer.29.0 4.17784350 43.31119170 + layer.29.1 4.17623735 33.56622841 + layer.39.0 10.57947434 1110.99331423 + layer.39.1 10.58388675 1105.79027380 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 293.43550296 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16986012 +BPFP 1.3203 bits/point +EBPFP 1.3203 equivalent bits/point +MSE 293.435503 +---------------------- --------------------------------------------------------- +Time: 23.220s Load: 1.150s, Pack+Encode: 8.256s, Decode+Unpack: 13.814s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 293.4355 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000442-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.292s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,724,408B, BPFP=1.0723 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,698,316B, BPFP=1.0560 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,231,408B, BPFP=1.3875 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,232,196B, BPFP=1.3880 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,466,800B, BPFP=1.5339 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,459,732B, BPFP=1.5295 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,571,168B, BPFP=0.9770 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,559,084B, BPFP=0.9695 +⌛️ [2/4] FRONTEND: Frontend time: 7.721s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.248s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 4.24042185 + layer.9.1 0.03247583 8.61016844 + layer.19.0 0.05000294 28.29582289 + layer.19.1 0.04728991 37.93294582 + layer.29.0 4.17616118 38.53644341 + layer.29.1 4.18555745 90.39231137 + layer.39.0 14.92630606 1089.13299904 + layer.39.1 15.22664209 1138.17255651 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 304.41420867 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15943112 +BPFP 1.2392 bits/point +EBPFP 1.2392 equivalent bits/point +MSE 304.414209 +---------------------- --------------------------------------------------------- +Time: 22.261s Load: 1.292s, Pack+Encode: 7.721s, Decode+Unpack: 13.248s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 304.4142 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000447-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.287s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,809,304B, BPFP=1.1251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,794,848B, BPFP=1.1161 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,264,692B, BPFP=1.4082 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,238,180B, BPFP=1.3917 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,318,480B, BPFP=1.4417 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,295,528B, BPFP=1.4274 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,388,464B, BPFP=0.8634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,388,448B, BPFP=0.8634 +⌛️ [2/4] FRONTEND: Frontend time: 7.984s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.135s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 8.95167866 + layer.9.1 0.11516861 28.74656757 + layer.19.0 0.04822375 80.14637058 + layer.19.1 0.02465675 74.76177969 + layer.29.0 0.12445424 31.46682237 + layer.29.1 4.21809243 67.85858405 + layer.39.0 56.99443848 1313.50955110 + layer.39.1 29.63154648 1251.51313276 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 357.11931085 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15497944 +BPFP 1.2046 bits/point +EBPFP 1.2046 equivalent bits/point +MSE 357.119311 +---------------------- --------------------------------------------------------- +Time: 22.405s Load: 1.287s, Pack+Encode: 7.984s, Decode+Unpack: 13.135s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 357.1193 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000455-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,780,656B, BPFP=1.1072 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,851,292B, BPFP=1.1512 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,329,688B, BPFP=1.4486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,393,512B, BPFP=1.4883 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,586,380B, BPFP=1.6083 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,694,944B, BPFP=1.6758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,716,424B, BPFP=1.0673 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,797,392B, BPFP=1.1176 +⌛️ [2/4] FRONTEND: Frontend time: 7.805s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 16.69883074 + layer.9.1 0.14323425 20.42371060 + layer.19.0 0.12097352 56.81575334 + layer.19.1 0.11863553 46.90199678 + layer.29.0 0.18810310 212.13363578 + layer.29.1 0.22084548 284.48113658 + layer.39.0 11.17468934 1197.50206940 + layer.39.1 12.52284677 1267.66857689 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 387.82821376 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17150288 +BPFP 1.3330 bits/point +EBPFP 1.3330 equivalent bits/point +MSE 387.828214 +---------------------- --------------------------------------------------------- +Time: 22.141s Load: 1.265s, Pack+Encode: 7.805s, Decode+Unpack: 13.071s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 387.8282 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000474-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.286s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,786,268B, BPFP=1.1107 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,788,968B, BPFP=1.1124 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,352,624B, BPFP=1.4629 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,352,884B, BPFP=1.4631 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,628,208B, BPFP=1.6343 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,628,764B, BPFP=1.6346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,735,948B, BPFP=1.0794 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,739,092B, BPFP=1.0814 +⌛️ [2/4] FRONTEND: Frontend time: 7.788s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.176s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 12.41011521 + layer.9.1 0.14176414 8.77719638 + layer.19.0 0.11837582 56.76567475 + layer.19.1 0.11399856 47.21789936 + layer.29.0 0.14311602 166.94677252 + layer.29.1 0.14520382 209.12856176 + layer.39.0 14.59939236 1288.99450812 + layer.39.1 17.09091825 1290.83771092 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 385.13480488 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17012756 +BPFP 1.3224 bits/point +EBPFP 1.3224 equivalent bits/point +MSE 385.134805 +---------------------- --------------------------------------------------------- +Time: 22.250s Load: 1.286s, Pack+Encode: 7.788s, Decode+Unpack: 13.176s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 385.1348 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000476-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.281s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,762,032B, BPFP=1.0957 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,777,180B, BPFP=1.1051 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,237,984B, BPFP=1.3916 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,275,744B, BPFP=1.4151 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,532,316B, BPFP=1.5746 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,562,216B, BPFP=1.5932 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,649,272B, BPFP=1.0255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,704,852B, BPFP=1.0601 +⌛️ [2/4] FRONTEND: Frontend time: 8.159s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.229s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 12.30588139 + layer.9.1 0.14209374 8.41650686 + layer.19.0 0.05177973 33.84025042 + layer.19.1 0.05586525 47.16531359 + layer.29.0 0.12731753 118.13267073 + layer.29.1 0.12791453 85.17908309 + layer.39.0 10.91882437 1148.75151226 + layer.39.1 9.86751520 1043.21513849 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 312.12579460 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16501596 +BPFP 1.2826 bits/point +EBPFP 1.2826 equivalent bits/point +MSE 312.125795 +---------------------- --------------------------------------------------------- +Time: 22.668s Load: 1.281s, Pack+Encode: 8.159s, Decode+Unpack: 13.229s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 312.1258 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000479-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.263s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,604,932B, BPFP=0.9980 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,628,668B, BPFP=1.0127 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,136,624B, BPFP=1.3286 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,168,152B, BPFP=1.3482 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,454,040B, BPFP=1.5260 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,532,048B, BPFP=1.5745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,651,988B, BPFP=1.0272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,704,672B, BPFP=1.0600 +⌛️ [2/4] FRONTEND: Frontend time: 7.881s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.128s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 8.17466944 + layer.9.1 0.03257298 4.37354184 + layer.19.0 0.03929411 41.79806889 + layer.19.1 0.03736255 33.27772803 + layer.29.0 4.19976128 66.49813953 + layer.29.1 4.19887364 80.62348774 + layer.39.0 17.81771704 1264.23296721 + layer.39.1 13.24929237 1218.37074180 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 339.66866806 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15881124 +BPFP 1.2344 bits/point +EBPFP 1.2344 equivalent bits/point +MSE 339.668668 +---------------------- --------------------------------------------------------- +Time: 22.271s Load: 1.263s, Pack+Encode: 7.881s, Decode+Unpack: 13.128s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 339.6687 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000489-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,932,008B, BPFP=1.2014 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,954,932B, BPFP=1.2156 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,426,988B, BPFP=1.5091 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,462,992B, BPFP=1.5315 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,676,832B, BPFP=1.6645 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,716,012B, BPFP=1.6889 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,748,756B, BPFP=1.0874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,755,020B, BPFP=1.0913 +⌛️ [2/4] FRONTEND: Frontend time: 7.981s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.179s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 44.73322588 + layer.9.1 0.14206870 36.78658767 + layer.19.0 0.11541664 51.56204732 + layer.19.1 0.11639375 56.42986410 + layer.29.0 4.18928181 54.09720730 + layer.29.1 4.20210771 39.46337999 + layer.39.0 272.14109758 1692.18147087 + layer.39.1 217.56435053 1595.87695002 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 446.39134164 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17673540 +BPFP 1.3737 bits/point +EBPFP 1.3737 equivalent bits/point +MSE 446.391342 +---------------------- --------------------------------------------------------- +Time: 22.451s Load: 1.291s, Pack+Encode: 7.981s, Decode+Unpack: 13.179s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 446.3913 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.283s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,909,108B, BPFP=1.1871 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,900,060B, BPFP=1.1815 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,420,416B, BPFP=1.5051 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,442,296B, BPFP=1.5187 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,759,232B, BPFP=1.7157 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,809,632B, BPFP=1.7471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,793,812B, BPFP=1.1154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,835,908B, BPFP=1.1416 +⌛️ [2/4] FRONTEND: Frontend time: 7.968s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 24.95527897 + layer.9.1 0.14265629 28.80418756 + layer.19.0 0.15235519 110.71155683 + layer.19.1 0.14002283 93.03793577 + layer.29.0 4.20702410 92.66726361 + layer.29.1 4.22502724 158.97811207 + layer.39.0 9.71896204 1145.17836676 + layer.39.1 14.02077861 1198.93290353 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 356.65820064 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17870464 +BPFP 1.3890 bits/point +EBPFP 1.3890 equivalent bits/point +MSE 356.658201 +---------------------- --------------------------------------------------------- +Time: 22.741s Load: 1.283s, Pack+Encode: 7.968s, Decode+Unpack: 13.490s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 356.6582 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000524-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.290s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,852,192B, BPFP=1.1517 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,844,080B, BPFP=1.1467 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,385,456B, BPFP=1.4833 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,334,960B, BPFP=1.4519 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,747,804B, BPFP=1.7086 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,647,600B, BPFP=1.6463 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,923,848B, BPFP=1.1963 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,894,496B, BPFP=1.1780 +⌛️ [2/4] FRONTEND: Frontend time: 7.985s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.551s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 20.34848078 + layer.9.1 0.14327397 8.51993046 + layer.19.0 0.03872790 37.54624821 + layer.19.1 0.03991431 5.90867197 + layer.29.0 0.11363128 67.83246777 + layer.29.1 0.09618797 18.96566330 + layer.39.0 113.00349212 1656.24403056 + layer.39.1 66.70960681 1495.98774276 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 413.91915448 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17630436 +BPFP 1.3704 bits/point +EBPFP 1.3704 equivalent bits/point +MSE 413.919154 +---------------------- --------------------------------------------------------- +Time: 22.825s Load: 1.290s, Pack+Encode: 7.985s, Decode+Unpack: 13.551s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 413.9192 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000536-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.297s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,130,320B, BPFP=1.3247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,102,568B, BPFP=1.3074 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,592,716B, BPFP=1.6122 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,562,224B, BPFP=1.5932 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,763,524B, BPFP=1.7184 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,710,544B, BPFP=1.6855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,552,380B, BPFP=0.9653 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,530,380B, BPFP=0.9516 +⌛️ [2/4] FRONTEND: Frontend time: 7.958s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.232s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 53.31333075 + layer.9.1 0.14239137 20.50629651 + layer.19.0 0.03888746 42.89645018 + layer.19.1 0.04246985 46.99232927 + layer.29.0 0.10356636 37.37578598 + layer.29.1 0.10009016 32.20477654 + layer.39.0 8.56607607 1083.91738300 + layer.39.1 7.91790657 997.09503343 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 289.28767321 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17944656 +BPFP 1.3948 bits/point +EBPFP 1.3948 equivalent bits/point +MSE 289.287673 +---------------------- --------------------------------------------------------- +Time: 22.487s Load: 1.297s, Pack+Encode: 7.958s, Decode+Unpack: 13.232s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 289.2877 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000546-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.283s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,800,220B, BPFP=1.1194 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,875,620B, BPFP=1.1663 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,202,220B, BPFP=1.3694 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,292,024B, BPFP=1.4252 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,307,344B, BPFP=1.4347 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,444,940B, BPFP=1.5203 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,432,416B, BPFP=0.8907 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,456,204B, BPFP=0.9055 +⌛️ [2/4] FRONTEND: Frontend time: 7.941s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.116s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 20.59416413 + layer.9.1 0.14243852 12.75181819 + layer.19.0 0.05701358 25.90089741 + layer.19.1 0.05730241 39.44899365 + layer.29.0 4.14713759 22.24048124 + layer.29.1 4.15440538 26.01031966 + layer.39.0 12.45677755 1300.36684177 + layer.39.1 14.71734096 1366.68704234 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 351.75006980 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15810988 +BPFP 1.2289 bits/point +EBPFP 1.2289 equivalent bits/point +MSE 351.750070 +---------------------- --------------------------------------------------------- +Time: 22.340s Load: 1.283s, Pack+Encode: 7.941s, Decode+Unpack: 13.116s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 351.7501 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000556-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.285s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,047,608B, BPFP=1.2732 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,024,144B, BPFP=1.2586 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,598,524B, BPFP=1.6158 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,575,788B, BPFP=1.6017 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,959,480B, BPFP=1.8403 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,924,368B, BPFP=1.8184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 2,033,700B, BPFP=1.2646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,983,588B, BPFP=1.2334 +⌛️ [2/4] FRONTEND: Frontend time: 8.028s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 29.15753094 + layer.9.1 0.11180697 53.41821872 + layer.19.0 0.09949989 88.49890560 + layer.19.1 0.11883939 62.14538065 + layer.29.0 0.15177689 299.51014804 + layer.29.1 0.14123031 226.65610076 + layer.39.0 349.58010984 2003.45972620 + layer.39.1 334.73010188 1936.01671442 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 587.35784067 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 19147200 +BPFP 1.4883 bits/point +EBPFP 1.4883 equivalent bits/point +MSE 587.357841 +---------------------- --------------------------------------------------------- +Time: 22.385s Load: 1.285s, Pack+Encode: 8.028s, Decode+Unpack: 13.073s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 587.3578 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000620-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,613,948B, BPFP=1.0036 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,549,252B, BPFP=0.9634 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,759,496B, BPFP=1.0941 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,606,480B, BPFP=0.9989 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,999,032B, BPFP=1.2430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,821,584B, BPFP=1.1327 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,352,364B, BPFP=0.8409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,278,072B, BPFP=0.7947 +⌛️ [2/4] FRONTEND: Frontend time: 8.067s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72630507 16.88924208 + layer.9.1 2.71889861 13.08869588 + layer.19.0 3.15508441 34.46779986 + layer.19.1 3.14332772 12.42232022 + layer.29.0 4.15805451 58.20796024 + layer.29.1 4.14588961 73.98383775 + layer.39.0 8.22539970 1102.95113021 + layer.39.1 8.64785859 1206.32394142 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 314.79186596 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12980228 +BPFP 1.0089 bits/point +EBPFP 1.0089 equivalent bits/point +MSE 314.791866 +---------------------- --------------------------------------------------------- +Time: 22.243s Load: 1.136s, Pack+Encode: 8.067s, Decode+Unpack: 13.040s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 314.7919 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000624-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.292s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,823,408B, BPFP=1.1338 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,924,336B, BPFP=1.1966 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,362,312B, BPFP=1.4689 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,482,040B, BPFP=1.5434 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,610,532B, BPFP=1.6233 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,743,324B, BPFP=1.7058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,626,632B, BPFP=1.0115 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,758,480B, BPFP=1.0935 +⌛️ [2/4] FRONTEND: Frontend time: 7.925s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.443s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 4.68560284 + layer.9.1 0.11119189 12.18302541 + layer.19.0 0.08174444 42.49951747 + layer.19.1 0.08249469 19.71061789 + layer.29.0 4.18188438 124.11247413 + layer.29.1 4.20908200 87.90425024 + layer.39.0 9.33443395 1151.13013372 + layer.39.1 9.53268950 1221.33285578 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 332.94480968 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17331064 +BPFP 1.3471 bits/point +EBPFP 1.3471 equivalent bits/point +MSE 332.944810 +---------------------- --------------------------------------------------------- +Time: 22.660s Load: 1.292s, Pack+Encode: 7.925s, Decode+Unpack: 13.443s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 332.9448 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.293s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,921,300B, BPFP=1.1947 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,955,156B, BPFP=1.2157 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,332,644B, BPFP=1.4505 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,335,888B, BPFP=1.4525 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,431,220B, BPFP=1.5118 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,423,132B, BPFP=1.5067 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,452,088B, BPFP=0.9029 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,489,424B, BPFP=0.9261 +⌛️ [2/4] FRONTEND: Frontend time: 7.920s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.345s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 17.00712726 + layer.9.1 0.03285184 25.29589253 + layer.19.0 0.04037820 19.57379965 + layer.19.1 0.04362713 24.27686993 + layer.29.0 0.11518513 115.86642391 + layer.29.1 0.11703357 49.42976958 + layer.39.0 256.78569723 1392.59949061 + layer.39.1 143.16752229 1286.98917542 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 366.37981861 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16340852 +BPFP 1.2701 bits/point +EBPFP 1.2701 equivalent bits/point +MSE 366.379819 +---------------------- --------------------------------------------------------- +Time: 22.558s Load: 1.293s, Pack+Encode: 7.920s, Decode+Unpack: 13.345s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 366.3798 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000676-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.291s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,972,372B, BPFP=1.2265 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,983,336B, BPFP=1.2333 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,396,364B, BPFP=1.4901 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,404,216B, BPFP=1.4950 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,512,392B, BPFP=1.5622 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,501,932B, BPFP=1.5557 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,500,596B, BPFP=0.9331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,456,900B, BPFP=0.9059 +⌛️ [2/4] FRONTEND: Frontend time: 7.949s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.162s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 41.78380691 + layer.9.1 0.11256296 97.73900629 + layer.19.0 0.03396921 60.50216392 + layer.19.1 0.04105656 60.45690564 + layer.29.0 4.20373127 61.05685888 + layer.29.1 4.19418701 52.51509770 + layer.39.0 8.83613586 1012.16523400 + layer.39.1 8.48765384 1050.41626870 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 304.57941776 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16728108 +BPFP 1.3002 bits/point +EBPFP 1.3002 equivalent bits/point +MSE 304.579418 +---------------------- --------------------------------------------------------- +Time: 22.402s Load: 1.291s, Pack+Encode: 7.949s, Decode+Unpack: 13.162s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 304.5794 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000678-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.287s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,853,188B, BPFP=1.1523 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,808,392B, BPFP=1.1245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,403,528B, BPFP=1.4946 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,332,640B, BPFP=1.4505 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,664,672B, BPFP=1.6569 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,620,504B, BPFP=1.6295 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,733,660B, BPFP=1.0780 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,691,596B, BPFP=1.0519 +⌛️ [2/4] FRONTEND: Frontend time: 7.826s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 12.38065604 + layer.9.1 0.03228644 16.71971879 + layer.19.0 0.12067159 29.45415722 + layer.19.1 0.11791951 47.57002646 + layer.29.0 0.15835167 164.39067972 + layer.29.1 0.15268422 184.41398042 + layer.39.0 158.29335801 1684.17462592 + layer.39.1 131.92238738 1788.15170328 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 490.90694348 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17108180 +BPFP 1.3298 bits/point +EBPFP 1.3298 equivalent bits/point +MSE 490.906943 +---------------------- --------------------------------------------------------- +Time: 22.591s Load: 1.287s, Pack+Encode: 7.826s, Decode+Unpack: 13.478s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 490.9069 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000684-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,826,864B, BPFP=1.1360 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,849,300B, BPFP=1.1499 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,312,092B, BPFP=1.4377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,338,148B, BPFP=1.4539 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,534,368B, BPFP=1.5759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,598,308B, BPFP=1.6157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,473,344B, BPFP=0.9161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,545,184B, BPFP=0.9608 +⌛️ [2/4] FRONTEND: Frontend time: 7.940s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.230s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 8.65169893 + layer.9.1 0.03230341 8.28936595 + layer.19.0 0.01113602 55.53047397 + layer.19.1 0.03747142 19.94970750 + layer.29.0 4.12172023 42.53891575 + layer.29.1 4.13913264 42.43220710 + layer.39.0 9.31610902 915.48002229 + layer.39.1 11.00762596 965.43369946 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 257.28826137 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16477608 +BPFP 1.2808 bits/point +EBPFP 1.2808 equivalent bits/point +MSE 257.288261 +---------------------- --------------------------------------------------------- +Time: 22.396s Load: 1.226s, Pack+Encode: 7.940s, Decode+Unpack: 13.230s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 257.2883 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000693-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.269s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,864,032B, BPFP=1.1591 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,833,452B, BPFP=1.1401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,413,276B, BPFP=1.5006 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,391,028B, BPFP=1.4868 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,682,092B, BPFP=1.6678 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,664,400B, BPFP=1.6568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,690,140B, BPFP=1.0510 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,685,988B, BPFP=1.0484 +⌛️ [2/4] FRONTEND: Frontend time: 8.056s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.192s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14187056 23.95180924 + layer.9.1 0.14241365 16.59518018 + layer.19.0 0.11657135 51.82000657 + layer.19.1 0.11473399 51.61880174 + layer.29.0 0.16421308 177.79974530 + layer.29.1 0.18111406 120.42288483 + layer.39.0 55.30549089 1577.39063992 + layer.39.1 49.87731316 1538.82744349 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 444.80331391 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17224408 +BPFP 1.3388 bits/point +EBPFP 1.3388 equivalent bits/point +MSE 444.803314 +---------------------- --------------------------------------------------------- +Time: 22.517s Load: 1.269s, Pack+Encode: 8.056s, Decode+Unpack: 13.192s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 444.8033 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000713-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.284s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,742,216B, BPFP=1.0833 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,800,648B, BPFP=1.1197 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,196,700B, BPFP=1.3659 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,262,480B, BPFP=1.4068 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,443,392B, BPFP=1.5193 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,468,416B, BPFP=1.5349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,561,288B, BPFP=0.9708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,509,328B, BPFP=0.9385 +⌛️ [2/4] FRONTEND: Frontend time: 7.765s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.101s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 4.50653373 + layer.9.1 0.03232725 4.57159966 + layer.19.0 0.03714494 5.60959139 + layer.19.1 0.03685654 6.23438619 + layer.29.0 4.16145554 17.85736280 + layer.29.1 4.17130075 22.51670447 + layer.39.0 7.63807493 1016.40639924 + layer.39.1 7.26751532 915.90417065 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 249.20084352 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15984468 +BPFP 1.2424 bits/point +EBPFP 1.2424 equivalent bits/point +MSE 249.200844 +---------------------- --------------------------------------------------------- +Time: 22.150s Load: 1.284s, Pack+Encode: 7.765s, Decode+Unpack: 13.101s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 249.2008 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000734-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.285s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,770,028B, BPFP=1.1006 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,818,044B, BPFP=1.1305 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,181,532B, BPFP=1.3565 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,192,804B, BPFP=1.3635 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,433,824B, BPFP=1.5134 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,393,352B, BPFP=1.4882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,463,028B, BPFP=0.9097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,482,008B, BPFP=0.9215 +⌛️ [2/4] FRONTEND: Frontend time: 8.154s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.409s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 21.21080070 + layer.9.1 0.14394252 29.57397375 + layer.19.0 0.03713998 28.15715785 + layer.19.1 0.11359857 136.36117081 + layer.29.0 4.20669858 38.52368374 + layer.29.1 0.11083615 75.63004417 + layer.39.0 7.41086201 1006.29823305 + layer.39.1 8.74303628 1088.32155365 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 303.00957721 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15734620 +BPFP 1.2230 bits/point +EBPFP 1.2230 equivalent bits/point +MSE 303.009577 +---------------------- --------------------------------------------------------- +Time: 22.848s Load: 1.285s, Pack+Encode: 8.154s, Decode+Unpack: 13.409s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 303.0096 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000737-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,923,928B, BPFP=1.1963 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,988,016B, BPFP=1.2362 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,507,472B, BPFP=1.5592 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,554,676B, BPFP=1.5885 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,828,224B, BPFP=1.7586 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,897,468B, BPFP=1.8017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,778,636B, BPFP=1.1060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,856,108B, BPFP=1.1542 +⌛️ [2/4] FRONTEND: Frontend time: 8.126s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 8.28843384 + layer.9.1 0.14198353 21.22800263 + layer.19.0 0.17418623 146.94073344 + layer.19.1 0.18921874 229.35426616 + layer.29.0 0.15243895 176.05410299 + layer.29.1 0.17994503 174.04691977 + layer.39.0 13.57905399 1182.68584846 + layer.39.1 8.80701993 1288.19062401 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 403.34861641 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18334528 +BPFP 1.4251 bits/point +EBPFP 1.4251 equivalent bits/point +MSE 403.348616 +---------------------- --------------------------------------------------------- +Time: 22.914s Load: 1.265s, Pack+Encode: 8.126s, Decode+Unpack: 13.522s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 403.3486 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000804-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,845,420B, BPFP=1.1475 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,840,068B, BPFP=1.1442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,438,856B, BPFP=1.5165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,468,520B, BPFP=1.5350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,781,864B, BPFP=1.7298 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,763,276B, BPFP=1.7183 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,752,844B, BPFP=1.0899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,695,836B, BPFP=1.0545 +⌛️ [2/4] FRONTEND: Frontend time: 8.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.914s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.72336357 8.74543400 + layer.9.1 2.61637510 8.56332453 + layer.19.0 0.14860626 257.55300860 + layer.19.1 0.15499876 258.45705985 + layer.29.0 0.29089499 457.37535817 + layer.29.1 0.20993857 301.71752627 + layer.39.0 12.63850088 1503.40512576 + layer.39.1 9.97545753 1383.05698822 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 522.35922817 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17586684 +BPFP 1.3670 bits/point +EBPFP 1.3670 equivalent bits/point +MSE 522.359228 +---------------------- --------------------------------------------------------- +Time: 23.377s Load: 1.162s, Pack+Encode: 8.302s, Decode+Unpack: 13.914s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 522.3592 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000816-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,968,820B, BPFP=1.2242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,013,360B, BPFP=1.2519 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,549,928B, BPFP=1.5856 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,570,876B, BPFP=1.5986 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,945,756B, BPFP=1.8317 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,912,740B, BPFP=1.8112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,862,208B, BPFP=1.1580 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,821,752B, BPFP=1.1328 +⌛️ [2/4] FRONTEND: Frontend time: 8.115s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.273s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 4.48236684 + layer.9.1 0.14187655 8.25317499 + layer.19.0 0.17405892 203.32018067 + layer.19.1 0.14315577 151.73945400 + layer.29.0 0.19218995 320.29059217 + layer.29.1 0.16272765 296.20455269 + layer.39.0 14.01399584 1348.19340974 + layer.39.1 9.48776763 1165.48957338 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 437.24666306 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18645440 +BPFP 1.4493 bits/point +EBPFP 1.4493 equivalent bits/point +MSE 437.246663 +---------------------- --------------------------------------------------------- +Time: 22.532s Load: 1.143s, Pack+Encode: 8.115s, Decode+Unpack: 13.273s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 437.2467 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000817-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,906,164B, BPFP=1.1853 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,798,792B, BPFP=1.1185 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,465,552B, BPFP=1.5331 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,362,788B, BPFP=1.4692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,674,744B, BPFP=1.6632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,564,988B, BPFP=1.5950 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,758,780B, BPFP=1.0936 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,699,064B, BPFP=1.0565 +⌛️ [2/4] FRONTEND: Frontend time: 8.227s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.429s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 24.38578976 + layer.9.1 0.14252999 12.29973038 + layer.19.0 0.12443910 96.98759352 + layer.19.1 0.13256963 115.71166627 + layer.29.0 4.20758094 38.81649455 + layer.29.1 4.18155761 48.30942574 + layer.39.0 45.67507362 1230.91053805 + layer.39.1 52.99942295 1225.13092964 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 349.06902099 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17230872 +BPFP 1.3393 bits/point +EBPFP 1.3393 equivalent bits/point +MSE 349.069021 +---------------------- --------------------------------------------------------- +Time: 22.946s Load: 1.289s, Pack+Encode: 8.227s, Decode+Unpack: 13.429s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 349.0690 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000880-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.270s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,788,636B, BPFP=1.1122 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,816,256B, BPFP=1.1294 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,318,328B, BPFP=1.4416 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,363,248B, BPFP=1.4695 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,525,596B, BPFP=1.5705 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,551,460B, BPFP=1.5865 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,636,260B, BPFP=1.0175 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,628,700B, BPFP=1.0128 +⌛️ [2/4] FRONTEND: Frontend time: 7.812s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 8.75384842 + layer.9.1 0.14194541 8.54901280 + layer.19.0 0.11782019 116.71507880 + layer.19.1 0.12099331 121.56698703 + layer.29.0 0.31534543 407.26472461 + layer.29.1 0.31351768 322.83321394 + layer.39.0 16.41217467 1384.86930914 + layer.39.1 11.15875965 1339.40432983 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 463.74456307 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16628484 +BPFP 1.2925 bits/point +EBPFP 1.2925 equivalent bits/point +MSE 463.744563 +---------------------- --------------------------------------------------------- +Time: 22.531s Load: 1.270s, Pack+Encode: 7.812s, Decode+Unpack: 13.448s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 463.7446 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,746,088B, BPFP=1.0857 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,699,148B, BPFP=1.0566 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,183,204B, BPFP=1.3576 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,051,188B, BPFP=1.2755 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,513,544B, BPFP=1.5630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,386,124B, BPFP=1.4837 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,642,564B, BPFP=1.0214 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,582,976B, BPFP=0.9843 +⌛️ [2/4] FRONTEND: Frontend time: 7.883s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.137s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 12.57809018 + layer.9.1 0.14279503 16.64923964 + layer.19.0 0.04409784 34.72794542 + layer.19.1 0.12204415 48.10175402 + layer.29.0 0.14332971 66.08792482 + layer.29.1 0.16018698 105.40978192 + layer.39.0 8.52841700 1187.41085642 + layer.39.1 19.04729908 1245.24952245 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 339.52688936 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15804836 +BPFP 1.2285 bits/point +EBPFP 1.2285 equivalent bits/point +MSE 339.526889 +---------------------- --------------------------------------------------------- +Time: 22.244s Load: 1.223s, Pack+Encode: 7.883s, Decode+Unpack: 13.137s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 339.5269 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000892-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.288s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,966,376B, BPFP=1.2227 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,942,864B, BPFP=1.2081 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,462,328B, BPFP=1.5311 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,427,368B, BPFP=1.5094 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,618,640B, BPFP=1.6283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,575,236B, BPFP=1.6013 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,561,864B, BPFP=0.9712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,534,968B, BPFP=0.9545 +⌛️ [2/4] FRONTEND: Frontend time: 8.190s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.271s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 4.51518133 + layer.9.1 0.03263012 4.59444270 + layer.19.0 0.05225635 78.37243812 + layer.19.1 0.04916960 47.41184137 + layer.29.0 4.19413323 79.93677869 + layer.29.1 4.20728930 89.58762138 + layer.39.0 8.98594322 1033.04194524 + layer.39.1 8.30659896 987.80834129 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 290.65857377 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17089644 +BPFP 1.3283 bits/point +EBPFP 1.3283 equivalent bits/point +MSE 290.658574 +---------------------- --------------------------------------------------------- +Time: 22.749s Load: 1.288s, Pack+Encode: 8.190s, Decode+Unpack: 13.271s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 290.6586 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000919-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.267s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,807,440B, BPFP=1.1239 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,784,888B, BPFP=1.1099 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,370,684B, BPFP=1.4741 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,322,008B, BPFP=1.4439 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,623,444B, BPFP=1.6313 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,620,884B, BPFP=1.6297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,718,652B, BPFP=1.0687 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,715,748B, BPFP=1.0669 +⌛️ [2/4] FRONTEND: Frontend time: 7.787s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.267s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258133 16.51300094 + layer.9.1 0.03283905 24.63605341 + layer.19.0 0.03703246 15.50547447 + layer.19.1 0.03684524 14.88480604 + layer.29.0 0.11326863 47.43296323 + layer.29.1 0.10834243 42.49583632 + layer.39.0 11.60468402 1227.55635148 + layer.39.1 14.87000682 1131.12663165 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 315.01888969 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16963748 +BPFP 1.3185 bits/point +EBPFP 1.3185 equivalent bits/point +MSE 315.018890 +---------------------- --------------------------------------------------------- +Time: 22.320s Load: 1.267s, Pack+Encode: 7.787s, Decode+Unpack: 13.267s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 315.0189 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000925-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,839,612B, BPFP=1.1439 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,849,100B, BPFP=1.1498 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,382,176B, BPFP=1.4813 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,382,360B, BPFP=1.4814 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,706,752B, BPFP=1.6831 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,691,136B, BPFP=1.6734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,768,724B, BPFP=1.0998 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,736,852B, BPFP=1.0800 +⌛️ [2/4] FRONTEND: Frontend time: 7.737s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.168s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 12.69842407 + layer.9.1 0.11188250 12.58146540 + layer.19.0 3.25906142 106.04712870 + layer.19.1 3.26015426 79.30397863 + layer.29.0 4.19564952 140.36021570 + layer.29.1 4.21244012 121.32171283 + layer.39.0 303.99934336 1844.79449220 + layer.39.1 331.94728988 1765.64358485 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 510.34387530 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17356712 +BPFP 1.3491 bits/point +EBPFP 1.3491 equivalent bits/point +MSE 510.343875 +---------------------- --------------------------------------------------------- +Time: 22.123s Load: 1.219s, Pack+Encode: 7.737s, Decode+Unpack: 13.168s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 510.3439 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000927-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.289s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,743,188B, BPFP=1.0839 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,738,148B, BPFP=1.0808 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,341,516B, BPFP=1.4560 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,323,632B, BPFP=1.4449 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,482,324B, BPFP=1.5435 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,471,848B, BPFP=1.5370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,557,616B, BPFP=0.9686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,531,404B, BPFP=0.9523 +⌛️ [2/4] FRONTEND: Frontend time: 8.010s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.178s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 4.25067218 + layer.9.1 0.00271392 4.27924340 + layer.19.0 3.19073251 10.49989678 + layer.19.1 3.15044721 14.89371916 + layer.29.0 4.17151372 17.36180630 + layer.29.1 4.17302847 22.24017530 + layer.39.0 85.12206503 1581.79687997 + layer.39.1 85.43754975 1509.76090417 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 395.63541216 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16189676 +BPFP 1.2584 bits/point +EBPFP 1.2584 equivalent bits/point +MSE 395.635412 +---------------------- --------------------------------------------------------- +Time: 22.477s Load: 1.289s, Pack+Encode: 8.010s, Decode+Unpack: 13.178s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 395.6354 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000942-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,826,264B, BPFP=1.1356 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,786,844B, BPFP=1.1111 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,353,960B, BPFP=1.4637 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,279,588B, BPFP=1.4175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,661,852B, BPFP=1.6552 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,534,536B, BPFP=1.5760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,796,120B, BPFP=1.1169 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,733,356B, BPFP=1.0778 +⌛️ [2/4] FRONTEND: Frontend time: 7.795s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.164s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 12.77081965 + layer.9.1 2.75948239 16.69421562 + layer.19.0 0.15224024 152.08032872 + layer.19.1 0.13045117 84.43846506 + layer.29.0 0.13097460 272.99757243 + layer.29.1 0.13177276 149.54286055 + layer.39.0 10.49186664 1304.28589621 + layer.39.1 12.55703299 1295.67693410 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 411.06088654 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16972520 +BPFP 1.3192 bits/point +EBPFP 1.3192 equivalent bits/point +MSE 411.060887 +---------------------- --------------------------------------------------------- +Time: 22.220s Load: 1.261s, Pack+Encode: 7.795s, Decode+Unpack: 13.164s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 411.0609 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000946-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,711,464B, BPFP=1.0642 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,745,300B, BPFP=1.0853 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,157,796B, BPFP=1.3418 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,197,816B, BPFP=1.3666 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,351,768B, BPFP=1.4624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,393,200B, BPFP=1.4881 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,549,580B, BPFP=0.9636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,570,164B, BPFP=0.9764 +⌛️ [2/4] FRONTEND: Frontend time: 7.796s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.404s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 8.31901912 + layer.9.1 0.03228249 4.24793588 + layer.19.0 0.04154089 61.36814510 + layer.19.1 0.04120101 65.74797039 + layer.29.0 4.21417063 46.90253900 + layer.29.1 4.21428318 45.33852674 + layer.39.0 28.58093312 1313.59232728 + layer.39.1 17.10356972 1239.34957020 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 348.10825421 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15677088 +BPFP 1.2185 bits/point +EBPFP 1.2185 equivalent bits/point +MSE 348.108254 +---------------------- --------------------------------------------------------- +Time: 22.351s Load: 1.151s, Pack+Encode: 7.796s, Decode+Unpack: 13.404s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 348.1083 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000959-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.042s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,895,332B, BPFP=1.1785 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,889,148B, BPFP=1.1747 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,455,212B, BPFP=1.5267 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,458,852B, BPFP=1.5290 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,741,968B, BPFP=1.7050 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,748,112B, BPFP=1.7088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,820,672B, BPFP=1.1321 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,823,868B, BPFP=1.1341 +⌛️ [2/4] FRONTEND: Frontend time: 8.327s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.914s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 16.44360624 + layer.9.1 0.14242138 24.69495185 + layer.19.0 0.13512425 101.93491324 + layer.19.1 0.13152432 120.59995821 + layer.29.0 0.11439834 106.26696315 + layer.29.1 0.11806111 120.67886422 + layer.39.0 18.41482236 1170.66658707 + layer.39.1 20.38586935 1171.50700414 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 354.09910601 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17833164 +BPFP 1.3861 bits/point +EBPFP 1.3861 equivalent bits/point +MSE 354.099106 +---------------------- --------------------------------------------------------- +Time: 23.283s Load: 1.042s, Pack+Encode: 8.327s, Decode+Unpack: 13.914s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 354.0991 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00000972-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.023s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,629,148B, BPFP=1.0130 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,646,812B, BPFP=1.0240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,159,184B, BPFP=1.3426 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,130,164B, BPFP=1.3246 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,404,864B, BPFP=1.4954 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,357,080B, BPFP=1.4657 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,649,444B, BPFP=1.0257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,569,240B, BPFP=0.9758 +⌛️ [2/4] FRONTEND: Frontend time: 8.268s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.767s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 4.87629897 + layer.9.1 0.14251336 4.22165668 + layer.19.0 0.11881898 34.13899833 + layer.19.1 0.11371834 43.10798711 + layer.29.0 0.15377442 128.39842009 + layer.29.1 0.16319071 108.94895137 + layer.39.0 9.10150218 1166.92390958 + layer.39.1 9.15265777 1215.82346387 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 338.30496075 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15545936 +BPFP 1.2083 bits/point +EBPFP 1.2083 equivalent bits/point +MSE 338.304961 +---------------------- --------------------------------------------------------- +Time: 23.057s Load: 1.023s, Pack+Encode: 8.268s, Decode+Unpack: 13.767s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 338.3050 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001000-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,686,048B, BPFP=1.0484 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,689,500B, BPFP=1.0506 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,085,932B, BPFP=1.2971 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,102,104B, BPFP=1.3071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,146,108B, BPFP=1.3345 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,147,880B, BPFP=1.3356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,553,368B, BPFP=0.9659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,531,672B, BPFP=0.9524 +⌛️ [2/4] FRONTEND: Frontend time: 8.281s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.746s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 12.64346919 + layer.9.1 0.14223260 8.67294950 + layer.19.0 0.05715554 107.14795248 + layer.19.1 0.06015340 72.02645455 + layer.29.0 0.19165729 227.74506527 + layer.29.1 0.21090307 213.09248647 + layer.39.0 19.07211701 1341.29051258 + layer.39.1 16.66110887 1305.63650111 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 411.03192389 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14942612 +BPFP 1.1614 bits/point +EBPFP 1.1614 equivalent bits/point +MSE 411.031924 +---------------------- --------------------------------------------------------- +Time: 23.048s Load: 1.022s, Pack+Encode: 8.281s, Decode+Unpack: 13.746s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 411.0319 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.025s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,856,108B, BPFP=1.1542 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,820,024B, BPFP=1.1317 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,453,820B, BPFP=1.5258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,415,572B, BPFP=1.5020 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,787,292B, BPFP=1.7332 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,725,944B, BPFP=1.6950 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,861,388B, BPFP=1.1574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,794,016B, BPFP=1.1155 +⌛️ [2/4] FRONTEND: Frontend time: 8.323s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.913s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 4.69659779 + layer.9.1 0.14288678 8.52840332 + layer.19.0 0.11144568 37.72533628 + layer.19.1 0.11742487 33.60802193 + layer.29.0 0.11418290 63.24857728 + layer.29.1 0.10734091 48.03104107 + layer.39.0 54.48020137 1466.08739255 + layer.39.1 66.40954314 1505.97293855 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 395.98728860 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17714164 +BPFP 1.3769 bits/point +EBPFP 1.3769 equivalent bits/point +MSE 395.987289 +---------------------- --------------------------------------------------------- +Time: 23.261s Load: 1.025s, Pack+Encode: 8.323s, Decode+Unpack: 13.913s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 395.9873 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001056-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,776,724B, BPFP=1.1048 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,756,552B, BPFP=1.0923 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,274,876B, BPFP=1.4146 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,235,240B, BPFP=1.3899 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,509,260B, BPFP=1.5603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,442,092B, BPFP=1.5185 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,687,592B, BPFP=1.0494 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,629,772B, BPFP=1.0134 +⌛️ [2/4] FRONTEND: Frontend time: 8.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.826s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 4.30913255 + layer.9.1 0.00081411 4.30146307 + layer.19.0 0.01015774 55.56721088 + layer.19.1 3.16362350 29.31784513 + layer.29.0 4.19769406 68.75395475 + layer.29.1 4.18061463 39.22960194 + layer.39.0 8.41366640 1079.55762496 + layer.39.1 8.38033145 1171.61397644 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 306.58135122 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16312108 +BPFP 1.2679 bits/point +EBPFP 1.2679 equivalent bits/point +MSE 306.581351 +---------------------- --------------------------------------------------------- +Time: 23.144s Load: 1.022s, Pack+Encode: 8.296s, Decode+Unpack: 13.826s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 306.5814 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001074-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,699,752B, BPFP=1.0569 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,716,200B, BPFP=1.0672 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,265,604B, BPFP=1.4088 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,264,732B, BPFP=1.4082 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,509,196B, BPFP=1.5603 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,544,140B, BPFP=1.5820 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,638,672B, BPFP=1.0190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,649,804B, BPFP=1.0259 +⌛️ [2/4] FRONTEND: Frontend time: 8.267s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.827s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 12.26231569 + layer.9.1 0.03271215 4.25492292 + layer.19.0 3.19210144 14.94400917 + layer.19.1 3.19171965 24.21643436 + layer.29.0 0.11530653 65.61556332 + layer.29.1 0.10966549 103.88606336 + layer.39.0 16.12381606 1166.54170646 + layer.39.1 25.33235335 1365.75135307 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 344.68404604 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16288100 +BPFP 1.2660 bits/point +EBPFP 1.2660 equivalent bits/point +MSE 344.684046 +---------------------- --------------------------------------------------------- +Time: 23.115s Load: 1.022s, Pack+Encode: 8.267s, Decode+Unpack: 13.827s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 344.6840 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001078-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.024s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,657,860B, BPFP=1.0309 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,704,332B, BPFP=1.0598 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,191,452B, BPFP=1.3627 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,281,020B, BPFP=1.4184 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,400,248B, BPFP=1.4925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,600,252B, BPFP=1.6169 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,635,932B, BPFP=1.0172 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,743,832B, BPFP=1.0843 +⌛️ [2/4] FRONTEND: Frontend time: 8.305s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.807s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64207787 4.25507837 + layer.9.1 0.03100527 4.25383909 + layer.19.0 3.19321449 15.09232977 + layer.19.1 3.20089330 24.47771659 + layer.29.0 0.10652387 81.68599272 + layer.29.1 0.17364564 345.64887775 + layer.39.0 9.89558772 1234.31462910 + layer.39.1 12.87769495 1389.01846546 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 387.34336611 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16214928 +BPFP 1.2603 bits/point +EBPFP 1.2603 equivalent bits/point +MSE 387.343366 +---------------------- --------------------------------------------------------- +Time: 23.136s Load: 1.024s, Pack+Encode: 8.305s, Decode+Unpack: 13.807s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 387.3434 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001086-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,681,416B, BPFP=1.0455 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,696,088B, BPFP=1.0547 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,295,376B, BPFP=1.4273 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,256,704B, BPFP=1.4033 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,576,564B, BPFP=1.6021 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,602,628B, BPFP=1.6184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,726,796B, BPFP=1.0737 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,761,780B, BPFP=1.0955 +⌛️ [2/4] FRONTEND: Frontend time: 8.323s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.872s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 4.21012603 + layer.9.1 0.03183258 4.21997715 + layer.19.0 0.03873757 23.97617200 + layer.19.1 0.03841183 14.75769809 + layer.29.0 0.10242378 81.72895774 + layer.29.1 0.10979955 138.49687599 + layer.39.0 11.55027136 1353.09773957 + layer.39.1 12.74680635 1533.26902260 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 394.21957115 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16597352 +BPFP 1.2901 bits/point +EBPFP 1.2901 equivalent bits/point +MSE 394.219571 +---------------------- --------------------------------------------------------- +Time: 23.226s Load: 1.031s, Pack+Encode: 8.323s, Decode+Unpack: 13.872s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 394.2196 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001102-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,955,576B, BPFP=1.2160 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,942,352B, BPFP=1.2078 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,564,388B, BPFP=1.5946 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,555,564B, BPFP=1.5891 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,831,968B, BPFP=1.7610 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,848,820B, BPFP=1.7714 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,900,708B, BPFP=1.1819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,916,560B, BPFP=1.1917 +⌛️ [2/4] FRONTEND: Frontend time: 8.324s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.963s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 8.21102393 + layer.9.1 0.03112686 8.32101764 + layer.19.0 0.03695946 28.96637466 + layer.19.1 0.03932408 24.31995682 + layer.29.0 0.11080087 134.11369787 + layer.29.1 0.12351766 167.06005253 + layer.39.0 27.63217079 1409.63435212 + layer.39.1 35.42625259 1414.11970710 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 399.34327283 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18515936 +BPFP 1.4392 bits/point +EBPFP 1.4392 equivalent bits/point +MSE 399.343273 +---------------------- --------------------------------------------------------- +Time: 23.308s Load: 1.022s, Pack+Encode: 8.324s, Decode+Unpack: 13.963s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 399.3433 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001107-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.025s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,777,956B, BPFP=1.1056 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,795,208B, BPFP=1.1163 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,344,176B, BPFP=1.4576 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,372,912B, BPFP=1.4755 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,665,376B, BPFP=1.6574 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,663,132B, BPFP=1.6560 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,770,040B, BPFP=1.1006 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,741,196B, BPFP=1.0827 +⌛️ [2/4] FRONTEND: Frontend time: 8.286s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.886s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 20.45927601 + layer.9.1 0.11126176 16.32929899 + layer.19.0 0.00622823 19.43440709 + layer.19.1 0.00986777 10.17851600 + layer.29.0 4.20227933 96.17669532 + layer.29.1 4.19170939 61.29369628 + layer.39.0 64.89367936 1235.83030882 + layer.39.1 48.85537050 1305.80937599 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 345.68894681 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17129996 +BPFP 1.3315 bits/point +EBPFP 1.3315 equivalent bits/point +MSE 345.688947 +---------------------- --------------------------------------------------------- +Time: 23.197s Load: 1.025s, Pack+Encode: 8.286s, Decode+Unpack: 13.886s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 345.6889 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001116-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,821,776B, BPFP=1.1328 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,834,320B, BPFP=1.1406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,339,968B, BPFP=1.4550 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,366,620B, BPFP=1.4716 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,595,248B, BPFP=1.6138 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,625,280B, BPFP=1.6324 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,652,860B, BPFP=1.0278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,673,004B, BPFP=1.0403 +⌛️ [2/4] FRONTEND: Frontend time: 8.200s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.264s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 4.37649733 + layer.9.1 0.03110840 4.38833392 + layer.19.0 0.11193399 42.60351003 + layer.19.1 0.11167925 19.87828319 + layer.29.0 0.13638519 152.97738578 + layer.29.1 0.13233996 120.75623806 + layer.39.0 10.36537055 1231.98400191 + layer.39.1 10.25938570 1217.54687997 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 349.31389128 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16909076 +BPFP 1.3143 bits/point +EBPFP 1.3143 equivalent bits/point +MSE 349.313891 +---------------------- --------------------------------------------------------- +Time: 22.485s Load: 1.021s, Pack+Encode: 8.200s, Decode+Unpack: 13.264s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 349.3139 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001125-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,805,812B, BPFP=1.1229 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,841,160B, BPFP=1.1449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,310,592B, BPFP=1.4368 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,359,876B, BPFP=1.4674 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,561,392B, BPFP=1.5927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,610,512B, BPFP=1.6233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,698,088B, BPFP=1.0559 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,738,840B, BPFP=1.0812 +⌛️ [2/4] FRONTEND: Frontend time: 7.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.615s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14239891 4.24692325 + layer.9.1 0.14185137 8.50923397 + layer.19.0 0.03937967 38.16442564 + layer.19.1 0.04081462 24.07390909 + layer.29.0 4.18784542 46.78447350 + layer.29.1 4.19318340 47.21642192 + layer.39.0 9.46241929 1110.30189430 + layer.39.1 9.25020271 1086.64836039 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 295.74320526 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16926272 +BPFP 1.3156 bits/point +EBPFP 1.3156 equivalent bits/point +MSE 295.743205 +---------------------- --------------------------------------------------------- +Time: 22.160s Load: 1.021s, Pack+Encode: 7.525s, Decode+Unpack: 13.615s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 295.7432 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001139-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,877,752B, BPFP=1.1676 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,876,128B, BPFP=1.1666 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,328,600B, BPFP=1.4480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,335,516B, BPFP=1.4523 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,537,840B, BPFP=1.5781 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,573,760B, BPFP=1.6004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,620,132B, BPFP=1.0074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,617,072B, BPFP=1.0055 +⌛️ [2/4] FRONTEND: Frontend time: 8.327s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.840s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 4.20991772 + layer.9.1 0.14180939 12.17807326 + layer.19.0 0.04123239 33.91458483 + layer.19.1 0.03889530 51.84563037 + layer.29.0 0.17016378 100.38531718 + layer.29.1 0.15026704 119.74535379 + layer.39.0 12.11620503 1248.90942375 + layer.39.1 10.53236554 1234.48034066 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 350.70858019 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16766800 +BPFP 1.3032 bits/point +EBPFP 1.3032 equivalent bits/point +MSE 350.708580 +---------------------- --------------------------------------------------------- +Time: 23.219s Load: 1.051s, Pack+Encode: 8.327s, Decode+Unpack: 13.840s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 350.7086 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001145-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.028s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,880,760B, BPFP=1.1695 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,846,860B, BPFP=1.1484 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,393,140B, BPFP=1.4881 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,320,604B, BPFP=1.4430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,671,656B, BPFP=1.6613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,601,448B, BPFP=1.6176 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,726,856B, BPFP=1.0738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,721,656B, BPFP=1.0706 +⌛️ [2/4] FRONTEND: Frontend time: 8.317s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.883s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 21.25231316 + layer.9.1 0.11141965 12.56380830 + layer.19.0 0.02960617 78.97242618 + layer.19.1 0.09893673 37.71306660 + layer.29.0 0.11288278 93.93099331 + layer.29.1 0.12156463 69.76850525 + layer.39.0 13.31952528 1402.46386501 + layer.39.1 8.92088009 1266.69348933 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 372.91980839 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17162980 +BPFP 1.3340 bits/point +EBPFP 1.3340 equivalent bits/point +MSE 372.919808 +---------------------- --------------------------------------------------------- +Time: 23.228s Load: 1.028s, Pack+Encode: 8.317s, Decode+Unpack: 13.883s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 372.9198 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001171-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,770,556B, BPFP=1.1010 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,798,920B, BPFP=1.1186 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,369,352B, BPFP=1.4733 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,408,980B, BPFP=1.4979 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,700,892B, BPFP=1.6795 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,734,964B, BPFP=1.7006 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,756,460B, BPFP=1.0922 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,709,348B, BPFP=1.0629 +⌛️ [2/4] FRONTEND: Frontend time: 8.204s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 4.38080341 + layer.9.1 0.03269095 12.29308690 + layer.19.0 0.03939078 64.97036175 + layer.19.1 0.03751187 47.41936784 + layer.29.0 0.14354374 129.54076130 + layer.29.1 0.12315212 91.60895216 + layer.39.0 10.67588198 1290.24036931 + layer.39.1 12.04857131 1203.95606495 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 355.55122095 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17249472 +BPFP 1.3408 bits/point +EBPFP 1.3408 equivalent bits/point +MSE 355.551221 +---------------------- --------------------------------------------------------- +Time: 22.679s Load: 1.022s, Pack+Encode: 8.204s, Decode+Unpack: 13.452s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 355.5512 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001179-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,792,420B, BPFP=1.1146 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,772,336B, BPFP=1.1021 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,386,924B, BPFP=1.4842 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,375,692B, BPFP=1.4772 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,647,388B, BPFP=1.6462 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,627,296B, BPFP=1.6337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,712,200B, BPFP=1.0647 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,691,784B, BPFP=1.0520 +⌛️ [2/4] FRONTEND: Frontend time: 8.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.859s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 4.37457250 + layer.9.1 0.03246013 4.24700969 + layer.19.0 0.05054442 89.77074379 + layer.19.1 0.04990058 74.62108504 + layer.29.0 4.26185866 213.28255333 + layer.29.1 4.26378007 156.74246856 + layer.39.0 11.04594849 1185.03183699 + layer.39.1 9.19037403 1193.40600127 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 365.18453390 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17006040 +BPFP 1.3218 bits/point +EBPFP 1.3218 equivalent bits/point +MSE 365.184534 +---------------------- --------------------------------------------------------- +Time: 23.205s Load: 1.045s, Pack+Encode: 8.302s, Decode+Unpack: 13.859s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 365.1845 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001184-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.033s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,847,496B, BPFP=1.1488 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,799,520B, BPFP=1.1190 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,357,936B, BPFP=1.4662 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,308,396B, BPFP=1.4354 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,665,632B, BPFP=1.6575 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,596,896B, BPFP=1.6148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,805,272B, BPFP=1.1225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,745,556B, BPFP=1.0854 +⌛️ [2/4] FRONTEND: Frontend time: 8.334s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.898s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 12.13850088 + layer.9.1 0.14317998 8.64503306 + layer.19.0 0.15093802 101.96320837 + layer.19.1 0.13472426 65.29819325 + layer.29.0 0.10723148 86.97158548 + layer.29.1 0.10832139 138.84218800 + layer.39.0 40.62415433 1287.94325056 + layer.39.1 9.85226018 1269.73535498 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 371.44216432 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17126704 +BPFP 1.3312 bits/point +EBPFP 1.3312 equivalent bits/point +MSE 371.442164 +---------------------- --------------------------------------------------------- +Time: 23.265s Load: 1.033s, Pack+Encode: 8.334s, Decode+Unpack: 13.898s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 371.4422 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001198-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,720,804B, BPFP=1.0700 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,735,404B, BPFP=1.0791 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,239,752B, BPFP=1.3927 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,256,056B, BPFP=1.4029 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,540,164B, BPFP=1.5795 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,584,996B, BPFP=1.6074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,750,604B, BPFP=1.0886 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,770,200B, BPFP=1.1007 +⌛️ [2/4] FRONTEND: Frontend time: 8.330s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.853s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 4.25475068 + layer.9.1 0.03106517 8.26040112 + layer.19.0 0.04795660 74.82851799 + layer.19.1 0.11462555 69.50078100 + layer.29.0 4.19919699 166.28762735 + layer.29.1 4.19569772 114.17659583 + layer.39.0 34.63583701 1250.82314550 + layer.39.1 33.06685271 1216.90735435 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 363.12989673 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16597980 +BPFP 1.2901 bits/point +EBPFP 1.2901 equivalent bits/point +MSE 363.129897 +---------------------- --------------------------------------------------------- +Time: 23.214s Load: 1.030s, Pack+Encode: 8.330s, Decode+Unpack: 13.853s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 363.1299 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001272-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,801,328B, BPFP=1.1201 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,850,936B, BPFP=1.1509 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,384,152B, BPFP=1.4825 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,441,724B, BPFP=1.5183 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,728,408B, BPFP=1.6966 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,820,524B, BPFP=1.7538 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,764,148B, BPFP=1.0970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,822,024B, BPFP=1.1330 +⌛️ [2/4] FRONTEND: Frontend time: 8.326s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.934s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 8.31531061 + layer.9.1 0.14287666 8.31375234 + layer.19.0 0.11209038 15.58932640 + layer.19.1 0.11164490 10.33142436 + layer.29.0 0.12578187 148.27952881 + layer.29.1 0.11401374 107.90439947 + layer.39.0 22.42121339 1494.40417065 + layer.39.1 25.87191330 1446.96864056 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 405.01331915 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17613244 +BPFP 1.3690 bits/point +EBPFP 1.3690 equivalent bits/point +MSE 405.013319 +---------------------- --------------------------------------------------------- +Time: 23.290s Load: 1.030s, Pack+Encode: 8.326s, Decode+Unpack: 13.934s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 405.0133 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001342-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,783,900B, BPFP=1.1093 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,810,412B, BPFP=1.1257 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,295,500B, BPFP=1.4274 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,341,376B, BPFP=1.4559 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,510,024B, BPFP=1.5608 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,583,184B, BPFP=1.6063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,627,912B, BPFP=1.0123 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,681,336B, BPFP=1.0455 +⌛️ [2/4] FRONTEND: Frontend time: 8.329s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.879s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 16.24135551 + layer.9.1 0.00120738 4.43259325 + layer.19.0 0.01953576 56.89170945 + layer.19.1 0.08568942 43.81766356 + layer.29.0 0.14491542 167.97556511 + layer.29.1 0.15694472 236.64740528 + layer.39.0 8.88920166 1097.34789876 + layer.39.1 9.38273353 1114.84089462 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 342.27438569 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16633644 +BPFP 1.2929 bits/point +EBPFP 1.2929 equivalent bits/point +MSE 342.274386 +---------------------- --------------------------------------------------------- +Time: 23.238s Load: 1.030s, Pack+Encode: 8.329s, Decode+Unpack: 13.879s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 342.2744 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001421-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.026s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,004,832B, BPFP=1.2466 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,990,284B, BPFP=1.2376 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,474,984B, BPFP=1.5390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,471,312B, BPFP=1.5367 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,706,856B, BPFP=1.6832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,688,088B, BPFP=1.6715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,724,092B, BPFP=1.0721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,716,604B, BPFP=1.0674 +⌛️ [2/4] FRONTEND: Frontend time: 8.339s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.891s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 123.59413801 + layer.9.1 0.14739036 119.67477515 + layer.19.0 0.16044666 220.07497612 + layer.19.1 0.14398357 192.08144301 + layer.29.0 0.50679369 449.64334607 + layer.29.1 0.43405572 346.02702165 + layer.39.0 123.83094556 1440.35959885 + layer.39.1 72.08861628 1446.68831582 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 542.26795184 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17777052 +BPFP 1.3818 bits/point +EBPFP 1.3818 equivalent bits/point +MSE 542.267952 +---------------------- --------------------------------------------------------- +Time: 23.256s Load: 1.026s, Pack+Encode: 8.339s, Decode+Unpack: 13.891s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 542.2680 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001428-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.026s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,795,008B, BPFP=1.1162 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,808,008B, BPFP=1.1242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,170,940B, BPFP=1.3499 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,175,596B, BPFP=1.3528 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,198,776B, BPFP=1.3672 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,192,880B, BPFP=1.3636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,416,256B, BPFP=0.8807 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,425,376B, BPFP=0.8863 +⌛️ [2/4] FRONTEND: Frontend time: 8.269s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.797s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 9.03421233 + layer.9.1 0.14229169 12.66299546 + layer.19.0 0.04567823 47.16514446 + layer.19.1 0.04432558 47.70959189 + layer.29.0 0.11507784 75.50137794 + layer.29.1 0.11363094 80.15337472 + layer.39.0 38.15331751 1294.23097740 + layer.39.1 50.78157832 1397.84574976 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 370.53792800 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15182840 +BPFP 1.1801 bits/point +EBPFP 1.1801 equivalent bits/point +MSE 370.537928 +---------------------- --------------------------------------------------------- +Time: 23.093s Load: 1.026s, Pack+Encode: 8.269s, Decode+Unpack: 13.797s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 370.5379 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001439-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.029s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,029,908B, BPFP=1.2622 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,074,984B, BPFP=1.2903 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,428,420B, BPFP=1.5100 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,478,704B, BPFP=1.5413 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,607,552B, BPFP=1.6214 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,678,192B, BPFP=1.6653 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,496,984B, BPFP=0.9308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,524,488B, BPFP=0.9480 +⌛️ [2/4] FRONTEND: Frontend time: 8.366s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.886s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 77.73203697 + layer.9.1 0.14417255 66.65018107 + layer.19.0 0.04986641 51.69688694 + layer.19.1 0.03935205 52.06066440 + layer.29.0 4.19438972 34.20459497 + layer.29.1 0.10069272 39.24239146 + layer.39.0 8.54645341 1136.13021331 + layer.39.1 8.58293537 1148.39637058 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 325.76416746 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17319232 +BPFP 1.3462 bits/point +EBPFP 1.3462 equivalent bits/point +MSE 325.764167 +---------------------- --------------------------------------------------------- +Time: 23.282s Load: 1.029s, Pack+Encode: 8.366s, Decode+Unpack: 13.886s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 325.7642 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001452-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.026s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,763,068B, BPFP=1.0963 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,767,008B, BPFP=1.0988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,357,852B, BPFP=1.4662 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,376,108B, BPFP=1.4775 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,665,240B, BPFP=1.6573 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,709,792B, BPFP=1.6850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,691,120B, BPFP=1.0516 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,792,384B, BPFP=1.1145 +⌛️ [2/4] FRONTEND: Frontend time: 8.313s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.874s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 8.54990884 + layer.9.1 0.14191958 12.53945549 + layer.19.0 0.11064845 37.77259929 + layer.19.1 0.11258393 37.51826399 + layer.29.0 0.14067722 236.49001114 + layer.29.1 0.15898021 174.96398440 + layer.39.0 18.90648132 1195.60633556 + layer.39.1 12.01175482 1201.31311684 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 363.09420944 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17122572 +BPFP 1.3309 bits/point +EBPFP 1.3309 equivalent bits/point +MSE 363.094209 +---------------------- --------------------------------------------------------- +Time: 23.212s Load: 1.026s, Pack+Encode: 8.313s, Decode+Unpack: 13.874s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 363.0942 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001464-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.026s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,781,704B, BPFP=1.1079 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,763,216B, BPFP=1.0964 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,365,696B, BPFP=1.4710 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,354,592B, BPFP=1.4641 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,657,648B, BPFP=1.6526 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,638,592B, BPFP=1.6407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,760,812B, BPFP=1.0949 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,738,264B, BPFP=1.0809 +⌛️ [2/4] FRONTEND: Frontend time: 8.336s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.893s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 16.16963770 + layer.9.1 0.03265336 4.38152441 + layer.19.0 0.11338584 55.30601819 + layer.19.1 0.11737041 60.73425561 + layer.29.0 0.14518043 189.39147564 + layer.29.1 0.15176190 170.81445002 + layer.39.0 10.84722720 1167.62074180 + layer.39.1 10.76635501 1109.52777778 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 346.74323514 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17060524 +BPFP 1.3261 bits/point +EBPFP 1.3261 equivalent bits/point +MSE 346.743235 +---------------------- --------------------------------------------------------- +Time: 23.255s Load: 1.026s, Pack+Encode: 8.336s, Decode+Unpack: 13.893s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 346.7432 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001478-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,845,896B, BPFP=1.1478 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,875,828B, BPFP=1.1664 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,397,348B, BPFP=1.4907 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,402,760B, BPFP=1.4941 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,671,368B, BPFP=1.6611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,644,284B, BPFP=1.6443 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,782,028B, BPFP=1.1081 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,801,980B, BPFP=1.1205 +⌛️ [2/4] FRONTEND: Frontend time: 8.327s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.898s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 12.07107609 + layer.9.1 0.14310633 8.43379646 + layer.19.0 0.11868409 56.36753323 + layer.19.1 0.12162521 78.59472501 + layer.29.0 0.16395149 280.63164597 + layer.29.1 0.12259847 78.43233644 + layer.39.0 330.19024594 2134.08978032 + layer.39.1 213.90321554 1803.17191977 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 556.47410166 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17421492 +BPFP 1.3541 bits/point +EBPFP 1.3541 equivalent bits/point +MSE 556.474102 +---------------------- --------------------------------------------------------- +Time: 23.247s Load: 1.022s, Pack+Encode: 8.327s, Decode+Unpack: 13.898s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 556.4741 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001495-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.024s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,781,156B, BPFP=1.1076 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,805,432B, BPFP=1.1226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,198,876B, BPFP=1.3673 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,239,560B, BPFP=1.3926 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,417,692B, BPFP=1.5034 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,499,756B, BPFP=1.5544 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,463,896B, BPFP=0.9103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,489,880B, BPFP=0.9264 +⌛️ [2/4] FRONTEND: Frontend time: 8.326s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.806s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 25.07858017 + layer.9.1 0.14187113 41.05649077 + layer.19.0 0.03719415 11.14809301 + layer.19.1 0.03715970 19.75040542 + layer.29.0 0.14992467 189.75031837 + layer.29.1 0.21581549 324.22254059 + layer.39.0 54.12547258 1251.86326011 + layer.39.1 37.28096148 1316.66045845 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 397.44126836 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15896248 +BPFP 1.2356 bits/point +EBPFP 1.2356 equivalent bits/point +MSE 397.441268 +---------------------- --------------------------------------------------------- +Time: 23.155s Load: 1.024s, Pack+Encode: 8.326s, Decode+Unpack: 13.806s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 397.4413 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001500-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,033,512B, BPFP=1.2645 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,024,764B, BPFP=1.2590 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,535,552B, BPFP=1.5766 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,538,868B, BPFP=1.5787 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,811,124B, BPFP=1.7480 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,809,824B, BPFP=1.7472 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,858,496B, BPFP=1.1556 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,827,448B, BPFP=1.1363 +⌛️ [2/4] FRONTEND: Frontend time: 8.384s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.939s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 36.76104843 + layer.9.1 0.14222666 37.54488021 + layer.19.0 0.12883153 98.52318131 + layer.19.1 0.12450899 116.29133835 + layer.29.0 0.12456659 158.76499323 + layer.29.1 0.12180437 163.76919174 + layer.39.0 16.93397679 1297.08205985 + layer.39.1 11.63264585 1330.37559694 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 404.88903626 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18439588 +BPFP 1.4333 bits/point +EBPFP 1.4333 equivalent bits/point +MSE 404.889036 +---------------------- --------------------------------------------------------- +Time: 23.343s Load: 1.021s, Pack+Encode: 8.384s, Decode+Unpack: 13.939s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 404.8890 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001520-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.025s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,655,516B, BPFP=1.0294 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,654,220B, BPFP=1.0286 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,076,316B, BPFP=1.2911 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,062,908B, BPFP=1.2827 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,189,152B, BPFP=1.3613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,194,484B, BPFP=1.3646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,407,484B, BPFP=0.8752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,452,384B, BPFP=0.9031 +⌛️ [2/4] FRONTEND: Frontend time: 8.223s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.756s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 24.78045159 + layer.9.1 0.14320703 13.28975645 + layer.19.0 0.18609190 202.96541706 + layer.19.1 0.20413370 207.01173989 + layer.29.0 0.16595908 129.23606137 + layer.29.1 0.17797341 169.60836517 + layer.39.0 9.44991518 1349.84447628 + layer.39.1 9.33992148 1320.61644381 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 427.16908895 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14692464 +BPFP 1.1420 bits/point +EBPFP 1.1420 equivalent bits/point +MSE 427.169089 +---------------------- --------------------------------------------------------- +Time: 23.004s Load: 1.025s, Pack+Encode: 8.223s, Decode+Unpack: 13.756s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 427.1691 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001571-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.023s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,798,464B, BPFP=1.1183 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,796,368B, BPFP=1.1170 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,104,056B, BPFP=1.3083 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,091,528B, BPFP=1.3005 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,290,656B, BPFP=1.4244 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,280,864B, BPFP=1.4183 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,383,516B, BPFP=0.8603 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,383,480B, BPFP=0.8603 +⌛️ [2/4] FRONTEND: Frontend time: 8.273s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.808s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 41.64162986 + layer.9.1 0.14264699 25.65488947 + layer.19.0 0.04840791 66.13600864 + layer.19.1 0.04358378 65.60914120 + layer.29.0 4.25626169 93.51532155 + layer.29.1 4.25716892 107.46637217 + layer.39.0 36.32893585 1175.26074499 + layer.39.1 22.75239275 1124.84041706 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 337.51556562 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15128932 +BPFP 1.1759 bits/point +EBPFP 1.1759 equivalent bits/point +MSE 337.515566 +---------------------- --------------------------------------------------------- +Time: 23.104s Load: 1.023s, Pack+Encode: 8.273s, Decode+Unpack: 13.808s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 337.5156 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001605-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,921,256B, BPFP=1.1947 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,923,844B, BPFP=1.1963 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,444,432B, BPFP=1.5200 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,428,040B, BPFP=1.5098 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,672,044B, BPFP=1.6615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,652,820B, BPFP=1.6496 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,785,412B, BPFP=1.1102 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,763,488B, BPFP=1.0966 +⌛️ [2/4] FRONTEND: Frontend time: 8.318s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.896s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 29.22437122 + layer.9.1 0.14259219 73.31144043 + layer.19.0 0.15398767 194.12551735 + layer.19.1 0.14449470 252.86168816 + layer.29.0 0.17467273 236.12434336 + layer.29.1 0.17545724 258.29025390 + layer.39.0 16.22751761 1315.36031519 + layer.39.1 26.19674268 1362.89652977 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 465.27430742 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17591336 +BPFP 1.3673 bits/point +EBPFP 1.3673 equivalent bits/point +MSE 465.274307 +---------------------- --------------------------------------------------------- +Time: 23.241s Load: 1.027s, Pack+Encode: 8.318s, Decode+Unpack: 13.896s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 465.2743 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001617-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,015,972B, BPFP=1.2536 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,026,780B, BPFP=1.2603 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,542,012B, BPFP=1.5807 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,560,752B, BPFP=1.5923 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,891,640B, BPFP=1.7981 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,921,264B, BPFP=1.8165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,932,556B, BPFP=1.2017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,992,268B, BPFP=1.2388 +⌛️ [2/4] FRONTEND: Frontend time: 8.380s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.998s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 17.10571001 + layer.9.1 0.14283950 32.75707378 + layer.19.0 0.09585176 139.14245065 + layer.19.1 0.13229247 120.54064191 + layer.29.0 0.10926771 49.74214522 + layer.29.1 0.10983113 40.82885377 + layer.39.0 13.84559555 1276.87496020 + layer.39.1 12.75833856 1341.06526584 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 377.25713767 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18883244 +BPFP 1.4677 bits/point +EBPFP 1.4677 equivalent bits/point +MSE 377.257138 +---------------------- --------------------------------------------------------- +Time: 23.409s Load: 1.030s, Pack+Encode: 8.380s, Decode+Unpack: 13.998s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 377.2571 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001630-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.032s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,144,484B, BPFP=1.3335 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,017,296B, BPFP=1.2544 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,601,052B, BPFP=1.6174 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,492,208B, BPFP=1.5497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,809,644B, BPFP=1.7471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,756,556B, BPFP=1.7141 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,822,340B, BPFP=1.1332 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,825,900B, BPFP=1.1354 +⌛️ [2/4] FRONTEND: Frontend time: 8.375s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.851s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 162.45523918 + layer.9.1 0.14345678 74.52211676 + layer.19.0 0.16166856 208.18765918 + layer.19.1 0.14880180 148.51743075 + layer.29.0 0.17070711 138.97076966 + layer.29.1 0.15868870 176.30891834 + layer.39.0 31.98565594 1299.39334607 + layer.39.1 38.57007372 1375.11031519 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 447.93322439 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18469480 +BPFP 1.4356 bits/point +EBPFP 1.4356 equivalent bits/point +MSE 447.933224 +---------------------- --------------------------------------------------------- +Time: 23.257s Load: 1.032s, Pack+Encode: 8.375s, Decode+Unpack: 13.851s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 447.9332 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001636-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,719,612B, BPFP=1.0693 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,694,284B, BPFP=1.0535 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,217,180B, BPFP=1.3787 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,224,532B, BPFP=1.3833 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,412,516B, BPFP=1.5001 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,417,700B, BPFP=1.5034 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,587,400B, BPFP=0.9871 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,632,212B, BPFP=1.0149 +⌛️ [2/4] FRONTEND: Frontend time: 7.502s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.737s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 8.22702886 + layer.9.1 0.03218400 8.19004883 + layer.19.0 0.03742503 32.99199101 + layer.19.1 0.04139693 34.08463666 + layer.29.0 0.11425402 96.61028534 + layer.29.1 0.11776626 105.59598854 + layer.39.0 23.31748448 1288.86930914 + layer.39.1 15.89369429 1266.61270296 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 355.14774892 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15905436 +BPFP 1.2363 bits/point +EBPFP 1.2363 equivalent bits/point +MSE 355.147749 +---------------------- --------------------------------------------------------- +Time: 22.324s Load: 1.084s, Pack+Encode: 7.502s, Decode+Unpack: 13.737s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 355.1477 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001639-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.238s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,970,400B, BPFP=1.2252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,983,032B, BPFP=1.2331 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,399,776B, BPFP=1.4922 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,414,488B, BPFP=1.5014 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,494,596B, BPFP=1.5512 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,518,964B, BPFP=1.5663 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,423,240B, BPFP=0.8850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,423,788B, BPFP=0.8853 +⌛️ [2/4] FRONTEND: Frontend time: 8.230s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.275s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 69.78797557 + layer.9.1 0.14315520 85.55926656 + layer.19.0 0.04114968 39.97752756 + layer.19.1 0.04120060 62.16281141 + layer.29.0 0.18627036 304.41439828 + layer.29.1 0.17990809 317.98237026 + layer.39.0 46.02158449 1452.98678765 + layer.39.1 44.38447151 1464.51337154 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 474.67306360 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16628284 +BPFP 1.2925 bits/point +EBPFP 1.2925 equivalent bits/point +MSE 474.673064 +---------------------- --------------------------------------------------------- +Time: 22.744s Load: 1.238s, Pack+Encode: 8.230s, Decode+Unpack: 13.275s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 474.6731 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001653-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.284s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,611,760B, BPFP=1.0022 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,650,764B, BPFP=1.0265 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,275,376B, BPFP=1.4149 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,295,208B, BPFP=1.4272 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,608,224B, BPFP=1.6218 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,624,496B, BPFP=1.6320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,699,804B, BPFP=1.0570 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,715,732B, BPFP=1.0669 +⌛️ [2/4] FRONTEND: Frontend time: 7.962s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.64482133 4.27231605 + layer.9.1 0.03141260 4.25915469 + layer.19.0 3.18767318 23.61473257 + layer.19.1 3.18914595 23.88923213 + layer.29.0 4.14946039 28.28966442 + layer.29.1 4.13952905 23.31916587 + layer.39.0 7.50609877 954.33412926 + layer.39.1 7.79272438 972.54274117 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 254.31514202 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16481364 +BPFP 1.2810 bits/point +EBPFP 1.2810 equivalent bits/point +MSE 254.315142 +---------------------- --------------------------------------------------------- +Time: 22.760s Load: 1.284s, Pack+Encode: 7.962s, Decode+Unpack: 13.513s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 254.3151 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001657-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.297s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,870,732B, BPFP=1.1633 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,843,424B, BPFP=1.1463 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,388,788B, BPFP=1.4854 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,371,632B, BPFP=1.4747 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,705,924B, BPFP=1.6826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,720,744B, BPFP=1.6918 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,625,464B, BPFP=1.0107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,626,968B, BPFP=1.0117 +⌛️ [2/4] FRONTEND: Frontend time: 8.239s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.725s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 37.37605709 + layer.9.1 0.14140505 40.58602953 + layer.19.0 0.11753838 96.99664717 + layer.19.1 0.11213660 38.28046651 + layer.29.0 0.21817993 289.39883795 + layer.29.1 4.26279853 184.18779847 + layer.39.0 8.71778059 1147.53541866 + layer.39.1 8.43609532 1108.51257561 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 367.85922887 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17153676 +BPFP 1.3333 bits/point +EBPFP 1.3333 equivalent bits/point +MSE 367.859229 +---------------------- --------------------------------------------------------- +Time: 23.261s Load: 1.297s, Pack+Encode: 8.239s, Decode+Unpack: 13.725s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 367.8592 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001659-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.274s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,915,120B, BPFP=1.1909 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,992,968B, BPFP=1.2393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,323,940B, BPFP=1.4451 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,435,988B, BPFP=1.5147 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,465,728B, BPFP=1.5332 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,632,576B, BPFP=1.6370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,584,140B, BPFP=0.9850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,693,832B, BPFP=1.0533 +⌛️ [2/4] FRONTEND: Frontend time: 7.974s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 50.07821454 + layer.9.1 0.11967093 133.45996498 + layer.19.0 0.14332279 80.55372990 + layer.19.1 0.14205440 94.58804919 + layer.29.0 0.15356100 94.93619667 + layer.29.1 0.14462723 141.89489812 + layer.39.0 8.04224558 1193.33524355 + layer.39.1 10.17930073 1208.01321235 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 374.60743866 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17044292 +BPFP 1.3248 bits/point +EBPFP 1.3248 equivalent bits/point +MSE 374.607439 +---------------------- --------------------------------------------------------- +Time: 22.688s Load: 1.274s, Pack+Encode: 7.974s, Decode+Unpack: 13.440s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 374.6074 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001671-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.293s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,691,396B, BPFP=1.0517 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,692,200B, BPFP=1.0522 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,157,316B, BPFP=1.3415 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,151,772B, BPFP=1.3380 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,373,552B, BPFP=1.4759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,319,104B, BPFP=1.4421 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,469,900B, BPFP=0.9140 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,488,196B, BPFP=0.9254 +⌛️ [2/4] FRONTEND: Frontend time: 8.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.160s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 4.39875742 + layer.9.1 0.00091860 8.21941534 + layer.19.0 3.15620088 32.47533379 + layer.19.1 3.15238324 15.23885456 + layer.29.0 4.13387767 23.75077603 + layer.29.1 4.13737010 18.66725864 + layer.39.0 41.03603550 1076.38244190 + layer.39.1 41.15380502 1073.07529449 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 281.52601652 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15343436 +BPFP 1.1926 bits/point +EBPFP 1.1926 equivalent bits/point +MSE 281.526017 +---------------------- --------------------------------------------------------- +Time: 22.586s Load: 1.293s, Pack+Encode: 8.134s, Decode+Unpack: 13.160s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 281.5260 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001694-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.269s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,814,936B, BPFP=1.1286 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,831,812B, BPFP=1.1391 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,225,008B, BPFP=1.3835 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,305,100B, BPFP=1.4333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,470,236B, BPFP=1.5360 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,622,704B, BPFP=1.6308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,641,340B, BPFP=1.0206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,677,804B, BPFP=1.0433 +⌛️ [2/4] FRONTEND: Frontend time: 7.761s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 29.07419761 + layer.9.1 0.14279730 33.51445101 + layer.19.0 0.12708100 75.11293179 + layer.19.1 0.11978473 129.04809376 + layer.29.0 0.14591184 176.94768784 + layer.29.1 0.16402206 282.31488777 + layer.39.0 105.60261461 1361.59678446 + layer.39.1 191.64541547 1604.30181471 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 461.48885612 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16588940 +BPFP 1.2894 bits/point +EBPFP 1.2894 equivalent bits/point +MSE 461.488856 +---------------------- --------------------------------------------------------- +Time: 22.485s Load: 1.269s, Pack+Encode: 7.761s, Decode+Unpack: 13.455s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.4889 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001712-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.286s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,913,404B, BPFP=1.1898 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,847,244B, BPFP=1.1486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,420,852B, BPFP=1.5053 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,367,580B, BPFP=1.4722 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,731,432B, BPFP=1.6984 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,665,580B, BPFP=1.6575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,821,040B, BPFP=1.1324 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,805,444B, BPFP=1.1227 +⌛️ [2/4] FRONTEND: Frontend time: 7.935s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 8.35866799 + layer.9.1 0.14187527 4.48197479 + layer.19.0 0.05966252 105.83653693 + layer.19.1 0.05602499 78.88552611 + layer.29.0 0.10851584 80.79077125 + layer.29.1 0.10663395 56.98349451 + layer.39.0 36.66006795 1576.18338109 + layer.39.1 37.39855191 1455.78653295 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 420.91336070 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17572576 +BPFP 1.3659 bits/point +EBPFP 1.3659 equivalent bits/point +MSE 420.913361 +---------------------- --------------------------------------------------------- +Time: 22.754s Load: 1.286s, Pack+Encode: 7.935s, Decode+Unpack: 13.533s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 420.9134 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001750-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.331s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,884,028B, BPFP=1.1715 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,871,432B, BPFP=1.1637 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,419,472B, BPFP=1.5045 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,436,572B, BPFP=1.5151 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,557,428B, BPFP=1.5903 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,621,940B, BPFP=1.6304 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,542,760B, BPFP=0.9593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,579,200B, BPFP=0.9820 +⌛️ [2/4] FRONTEND: Frontend time: 8.231s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.259s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 12.50097003 + layer.9.1 0.11247108 4.27450329 + layer.19.0 0.01001183 90.12874085 + layer.19.1 3.17262087 48.18435610 + layer.29.0 0.16690336 99.92673512 + layer.29.1 0.17317613 92.71687958 + layer.39.0 33.55914965 1457.61763770 + layer.39.1 10.63762287 1261.32123528 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 383.33388224 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16912832 +BPFP 1.3146 bits/point +EBPFP 1.3146 equivalent bits/point +MSE 383.333882 +---------------------- --------------------------------------------------------- +Time: 22.820s Load: 1.331s, Pack+Encode: 8.231s, Decode+Unpack: 13.259s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 383.3339 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.277s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,834,672B, BPFP=1.1408 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,913,296B, BPFP=1.1897 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,309,236B, BPFP=1.4359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,390,080B, BPFP=1.4862 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,542,464B, BPFP=1.5809 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,670,208B, BPFP=1.6604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,643,712B, BPFP=1.0221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,735,900B, BPFP=1.0794 +⌛️ [2/4] FRONTEND: Frontend time: 8.248s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.262s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 4.61767625 + layer.9.1 0.03247940 16.28323235 + layer.19.0 0.20408508 260.67657593 + layer.19.1 0.20919449 315.67032792 + layer.29.0 0.13400092 181.83289557 + layer.29.1 0.12260655 143.71673034 + layer.39.0 13.98719058 1279.62822350 + layer.39.1 8.64389327 1238.93083413 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 430.16956200 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17039568 +BPFP 1.3244 bits/point +EBPFP 1.3244 equivalent bits/point +MSE 430.169562 +---------------------- --------------------------------------------------------- +Time: 22.787s Load: 1.277s, Pack+Encode: 8.248s, Decode+Unpack: 13.262s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 430.1696 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001838-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.276s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,932,596B, BPFP=1.2017 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,954,000B, BPFP=1.2150 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,407,348B, BPFP=1.4969 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,431,856B, BPFP=1.5122 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,610,624B, BPFP=1.6233 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,628,716B, BPFP=1.6346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,686,136B, BPFP=1.0485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,655,672B, BPFP=1.0295 +⌛️ [2/4] FRONTEND: Frontend time: 8.105s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.310s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 13.72341611 + layer.9.1 0.14463072 66.30084965 + layer.19.0 0.16931463 169.67231773 + layer.19.1 0.17979540 179.76339144 + layer.29.0 0.11737749 98.99049865 + layer.29.1 0.10948915 105.12221426 + layer.39.0 8.46774266 1018.44094238 + layer.39.1 8.48397517 1010.44850366 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 332.80776673 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17306948 +BPFP 1.3452 bits/point +EBPFP 1.3452 equivalent bits/point +MSE 332.807767 +---------------------- --------------------------------------------------------- +Time: 22.692s Load: 1.276s, Pack+Encode: 8.105s, Decode+Unpack: 13.310s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 332.8078 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001840-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.298s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,902,888B, BPFP=1.1832 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,912,600B, BPFP=1.1893 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,460,848B, BPFP=1.5302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,474,828B, BPFP=1.5389 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,787,548B, BPFP=1.7333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,801,068B, BPFP=1.7417 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,796,940B, BPFP=1.1174 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,770,428B, BPFP=1.1009 +⌛️ [2/4] FRONTEND: Frontend time: 8.000s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14223057 24.83766117 + layer.9.1 0.14268742 41.44675263 + layer.19.0 0.21739516 322.78096148 + layer.19.1 0.24972380 322.94858325 + layer.29.0 0.18828982 327.00037806 + layer.29.1 0.18108670 294.71201449 + layer.39.0 11.67542184 1309.65631964 + layer.39.1 15.11985385 1329.07433938 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 496.55712626 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17907148 +BPFP 1.3919 bits/point +EBPFP 1.3919 equivalent bits/point +MSE 496.557126 +---------------------- --------------------------------------------------------- +Time: 22.350s Load: 1.298s, Pack+Encode: 8.000s, Decode+Unpack: 13.051s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 496.5571 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001854-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.295s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,845,692B, BPFP=1.1477 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,843,228B, BPFP=1.1461 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,381,896B, BPFP=1.4811 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,367,572B, BPFP=1.4722 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,684,248B, BPFP=1.6691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,651,216B, BPFP=1.6486 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,678,604B, BPFP=1.0438 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,666,836B, BPFP=1.0365 +⌛️ [2/4] FRONTEND: Frontend time: 7.921s (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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.191s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 20.29110703 + layer.9.1 0.14270393 4.50582238 + layer.19.0 0.11367196 94.12719874 + layer.19.1 0.12267420 79.37783051 + layer.29.0 0.13560262 62.85602714 + layer.29.1 0.14809222 185.97067017 + layer.39.0 10.32325245 1155.30404330 + layer.39.1 8.35688960 1157.20996498 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 344.95533303 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17119292 +BPFP 1.3306 bits/point +EBPFP 1.3306 equivalent bits/point +MSE 344.955333 +---------------------- --------------------------------------------------------- +Time: 22.408s Load: 1.295s, Pack+Encode: 7.921s, Decode+Unpack: 13.191s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 344.9553 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001855-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.292s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,683,876B, BPFP=1.0471 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,685,188B, BPFP=1.0479 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,176,436B, BPFP=1.3533 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,204,704B, BPFP=1.3709 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,335,548B, BPFP=1.4523 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,399,692B, BPFP=1.4922 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,535,380B, BPFP=0.9547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,539,136B, BPFP=0.9571 +⌛️ [2/4] FRONTEND: Frontend time: 7.829s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.420s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.61171023 12.60691012 + layer.9.1 2.72679972 8.40238106 + layer.19.0 0.11263356 75.45355778 + layer.19.1 0.10212393 62.27702662 + layer.29.0 4.19513435 62.94799128 + layer.29.1 4.21594343 109.37411453 + layer.39.0 8.80532175 1353.62527857 + layer.39.1 9.27097449 1306.01910220 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 373.83829527 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15559960 +BPFP 1.2094 bits/point +EBPFP 1.2094 equivalent bits/point +MSE 373.838295 +---------------------- --------------------------------------------------------- +Time: 22.541s Load: 1.292s, Pack+Encode: 7.829s, Decode+Unpack: 13.420s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 373.8383 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001857-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 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([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,908,696B, BPFP=1.1869 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,935,420B, BPFP=1.2035 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,368,832B, BPFP=1.4730 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,418,132B, BPFP=1.5036 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,584,584B, BPFP=1.6071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,645,772B, BPFP=1.6452 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,632,712B, BPFP=1.0152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,628,824B, BPFP=1.0128 +⌛️ [2/4] FRONTEND: Frontend time: 7.796s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.076s + +[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, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 150.65360355 + layer.9.1 0.14997165 113.57115568 + layer.19.0 0.15685862 179.34061605 + layer.19.1 0.13652294 93.89738539 + layer.29.0 0.22636045 231.88439191 + layer.29.1 0.21023706 254.82863738 + layer.39.0 31.35143565 1474.63451130 + layer.39.1 33.65704095 1429.60617638 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 491.05205970 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17122972 +BPFP 1.3309 bits/point +EBPFP 1.3309 equivalent bits/point +MSE 491.052060 +---------------------- --------------------------------------------------------- +Time: 22.097s Load: 1.225s, Pack+Encode: 7.796s, Decode+Unpack: 13.076s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 491.0521 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001891-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.268s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,794,956B, BPFP=1.1161 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,814,424B, BPFP=1.1282 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,322,356B, BPFP=1.4441 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,341,704B, BPFP=1.4561 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,651,048B, BPFP=1.6485 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,660,276B, BPFP=1.6542 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,747,388B, BPFP=1.0866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,733,504B, BPFP=1.0779 +⌛️ [2/4] FRONTEND: Frontend time: 7.868s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.099s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 24.73330299 + layer.9.1 0.14194651 12.70535856 + layer.19.0 0.13165920 84.36715019 + layer.19.1 0.11547583 88.98908588 + layer.29.0 4.19202371 171.91887536 + layer.29.1 0.11136677 77.18841034 + layer.39.0 9.51575185 1175.18489335 + layer.39.1 9.66679849 1159.69842407 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 349.34818759 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17065656 +BPFP 1.3265 bits/point +EBPFP 1.3265 equivalent bits/point +MSE 349.348188 +---------------------- --------------------------------------------------------- +Time: 22.234s Load: 1.268s, Pack+Encode: 7.868s, Decode+Unpack: 13.099s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 349.3482 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst... + +Original data structure: +root: [dict] with 4 keys + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 1.252s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,685,076B, BPFP=1.0478 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,680,440B, BPFP=1.0449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,065,844B, BPFP=1.2846 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,060,740B, BPFP=1.2814 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,018,584B, BPFP=1.2552 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,013,724B, BPFP=1.2522 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,279,612B, BPFP=0.7957 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,299,908B, BPFP=0.8083 +⌛️ [2/4] FRONTEND: Frontend time: 8.083s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 13.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 2.60361947 8.39691032 + layer.9.1 2.64162177 20.43638322 + layer.19.0 3.15421573 25.21274823 + layer.19.1 3.18597002 34.68625637 + layer.29.0 4.16148507 25.72554272 + layer.29.1 4.16879732 21.33547238 + layer.39.0 7.32495125 1122.75644699 + layer.39.1 7.16856507 1125.16817892 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 297.96474239 + (elements=102,924,288) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14103928 +BPFP 1.0963 bits/point +EBPFP 1.0963 equivalent bits/point +MSE 297.964742 +---------------------- --------------------------------------------------------- +Time: 22.402s Load: 1.252s, Pack+Encode: 8.083s, Decode+Unpack: 13.066s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 297.9647 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.3056 bits/point +Avg EBPFP 1.3056 equivalent bits/point +Avg MSE 377.351952 +Avg Time 22.741s +------------------------ ----------------------------